Het Preventiespel en zijn versies

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Het Preventiespel en zijn versies

Labor improbus omnia vincit (Het onvermoeid werken overwint alles)

Het Preventiespel en zijn versies Document bedoeld voor de ontwerpers van de spel versies

“Du choc des idées jaillit la lumière” (“Het botsen van de ideeën werpt licht op de zaak”)

Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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1

Inhoudstafel

1 Inhoudstafel....................................................................................................................2 2 Algemene Informatie en Overzicht................................................................................6 2.1 Kruisverwijzingen naar delen in dit document en links naar Web pagina's...........6 2.2 Overzicht.................................................................................................................7 2.3Versies van dit document.........................................................................................9 3 Het leren en e-leren, door volwassenen, en met educatieve spellen, simulaties en microwerelden.................................................................................................................12 3.1 Introductie.............................................................................................................12 3.1.1 Definities die niet door de beschouwde auteurs gegeven worden................12 3.1.1.1 “Associationism”...................................................................................12 3.1.1.2 “Behaviorism”.......................................................................................12 3.1.1.3 “Connectionism”...................................................................................12 3.1.1.4 “Constructivism”...................................................................................13 3.1.1.5 “Explicit learning”.................................................................................13 3.1.1.6 “Implicit learning”.................................................................................13 3.1.1.7 “Instruction”..........................................................................................13 3.2 T. Mayes en S. de Freitas: Leren en e-Leren, de rol van theorie..........................13 3.2.1 [T. Mayes en S. de Freitas] Introductie.........................................................13 3.2.2 [T. Mayes en S. de Freitas] The need for theory...........................................14 3.2.3 [T. Mayes en S. de Freitas] Learning theory and pedagogical design..........14 3.2.3.1 [T. Mayes en S. de Freitas] The associationist perspective...................15 3.2.3.2 [T. Mayes en S. de Freitas] The cognitive perspective.........................15 3.2.3.3 [T. Mayes en S. de Freitas] The situative perspective...........................16 3.2.4 [T. Mayes en S. de Freitas] E-learning and the learning cycle.....................16 3.2.5 [T. Mayes en S. de Freitas] Geciteerde bronnen...........................................18 3.3D. Jaques en G. Salmon: Leren in groepen............................................................18 3.3.1 [D. Jaques en G. Salmon] Introductie...........................................................18 3.3.2 [D. Jaques en G. Salmon] Groepen en Teams..............................................20 3.3.2.1 Groepen.................................................................................................20 3.3.2.2 Teams....................................................................................................20 3.3.3[D. Jaques en G. Salmon] The “Tavistock” model........................................20 3.3.4 [D. Jaques en G. Salmon] Group analysis.....................................................22 3.4 M.S. Knowles (1913-1997) over het leren door volwassenen..............................23 3.4.1 [M.S. Knowles] Introductie...........................................................................23 3.4.2 [M.S. Knowles] The Modern Practice of Adult Education...........................23 3.4.3 [M.S. Knowles] Een theorie betreffende het leren door volwassenen: Andragogie en het andragogisch Model (Theory of learning)...............................24 3.4.3.1 [M.S. Knowles] The need to know........................................................25 3.4.3.2 [M.S. Knowles] The learners' self-concept...........................................25 3.4.3.3 [M.S. Knowles] The role of the learners' experiences..........................25 3.4.3.4 [M.S. Knowles] Readiness to learn.......................................................26 3.4.3.5 [M.S. Knowles] Orientation to learning................................................26 3.4.3.6 [M.S. Knowles] Motivation...................................................................26 3.4.4 [M.S. Knowles] Theories of teaching...........................................................27 3.4.4.1 [M.S. Knowles] Het helpen van volwassenen om te leren....................27 3.4.4.2 [M.S. Knowles] Leren via onderzoek door de lerende (Teaching [Facilitating] through inquiry)...........................................................................28 Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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3.4.5 [M.S. Knowles] Aanvaarding van de theorieën en resultaten van onderzoek betreffende de theorieën.........................................................................................29 3.4.5.1 [M.S. Knowles] The learner's need to know.........................................30 3.4.5.2 [M.S. Knowles] Self-directed learning..................................................30 3.4.5.3 [M.S. Knowles] Prior experiences of the learner..................................31 3.4.5.4 [M.S. Knowles] Readiness to learn.......................................................32 3.4.5.5 [M.S. Knowles] Orientation to learning: Problem solving....................32 3.4.5.6 [M.S. Knowles] Motivation to learn......................................................33 3.4.6 [M.S. Knowles] Geciteerde bronnen.............................................................33 3.5 L.P. Rieber over Educatieve Spellen, Simulaties, en Microwerelden .................34 3.5.1 Definities gegeven door L.P. Rieber.............................................................35 3.5.2 [L.P. Rieber] Educatieve simulaties en ermee leren.....................................35 3.5.2.1 [L.P. Rieber] De 2 onderzoeksdomeinen in verband met educatieve simulaties...........................................................................................................35 3.5.2.2 [L.P. Rieber] Het eerste onderzoeksdomein in verband met educatieve simulaties...........................................................................................................36 3.5.2.3 [L.P. Rieber] Het tweede onderzoeksdomein in verband met educatieve simulaties...........................................................................................................38 3.5.3 [L.P. Rieber] Micro werelden en ermee leren...............................................39 3.5.4 [L.P. Rieber] Educatieve spellen en ermee leren..........................................41 3.5.5 [L.P. Rieber] Geciteerde bronnen.................................................................42 4 De ontstaansgeschiedenis van het Preventiespel..........................................................44 4.1 Mijn vorming en loopbaan....................................................................................44 4.2 Hoe mijn interesse voor de drugsproblematiek begon..........................................45 4.3 Mijn 'mentor' en verdere ontwikkelingen.............................................................46 4.4 Het “Slimmerkoken” intermezzo..........................................................................47 4.5 En nu, hoe gaat het verder met het Preventiespel?...............................................48 5 De 'papier en karton' kaartspel versie...........................................................................50 5.1 Waarom.................................................................................................................50 5.2 Wat........................................................................................................................50 5.2.1 Beperkingen..................................................................................................50 5.2.2 Het spel moet in overeenstemming zijn met de beproefde theorieën over leren........................................................................................................................50 5.2.2.1 In overeenstemming met de beproefde theorieën over leren in 't algemeen............................................................................................................50 5.2.2.2 In overeenstemming met de beproefde theorieën over leren door volwassenen en het faciliteren ervan.................................................................51 5.2.2.3 In overeenstemming met de beproefde theorieën over leren aan de hand van een spel.......................................................................................................53 5.2.3 Elementen van het 'papier en karton' kaartspel.............................................53 5.2.3.1 De 'spelers'.............................................................................................53 5.2.3.2 Cases en case kaarten............................................................................54 5.2.3.2.1 'Feit' cases en kaarten.....................................................................54 5.2.3.2.2 'Enkel groepsdiscussie' cases en kaarten........................................54 5.2.3.2.3 Kaartenbundels..............................................................................55 5.2.3.3 Fazen en thema's....................................................................................55 5.2.3.3.1 Fazen in de ontwikkeling van een kind.........................................55 5.2.3.3.2 Thema's..........................................................................................55 5.2.3.4 Selectie van kaarten bij het begin van een spelsessie............................56 5.2.3.4.1 De kaartenstapel.............................................................................56



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5.2.3.4.2 Selectie van kaarten/bundels voor het spelen van een bepaalde fase ......................................................................................................................56 5.2.3.4.3 Selectie van kaarten/bundels voor het spelen van een thema........57 5.2.4Details............................................................................................................58 6 De online kaartspel versie.............................................................................................59 6.1 Wat wordt hier bedoeld met 'online' ?..................................................................59 6.2 Waarom: voordelen van de online kaartspel versie..............................................60 6.3 Wat........................................................................................................................61 6.3.1 Het spel moet in overeenstemming zijn met de beproefde theorieën over leren........................................................................................................................61 6.3.2 Registratie van spelers...................................................................................61 6.3.3 Elementen van het spel..................................................................................61 6.3.4 Extra voorzieningen......................................................................................61 6.3.5 Details...........................................................................................................61 7 De 'virtueel kind' online versie.....................................................................................62 7.1 Voorlopige korte omschrijving.............................................................................62 8 De 'luchtkasteel'? 'virtueel kind' online versie..............................................................63 8.1 Voorlopige korte omschrijving.............................................................................63 9 Hoe het spel gebruiken in het kader van het grotere geheel van de preventie..............64 9.1 Een aantal bronnen die ik al zeker zal gebruiken:................................................64 9.1.1 “Drug Abuse Prevention Through Family Interventions”............................64 9.1.2 “Laat ze niet schieten! - Geef de grens een plaats in het leven van jongeren” ................................................................................................................................64 9.1.3 “Harlem Children's Zone”.............................................................................64 10 Referenties naar Boeken, Web documenten en Web sites.........................................66 10.1 “Dictionary of Psychology”................................................................................66 10.2 “Drug Abuse Prevention Through Family Interventions”..................................66 10.3 “Harlem Children's Zone”..................................................................................66 10.4 “Informatie over Drugs inclusief Tabak en Alcohol voor residents in Spanje (DTA)”........................................................................................................................66 10.5 “Learning in Groups - A Handbook for face-to-face and online environments” .....................................................................................................................................66 10.6 “Laat ze niet schieten! - Geef de grens een plaats in het leven van jongeren”...66 10.7 “Rethinking Pedagogy for a digital age”............................................................66 10.8 “Specificatie van het Preventiespel als 'papier en karton' kaartspel”..................66 10.9 “The Adult Learner”...........................................................................................66 10.10 “The Cambridge Handbook of Multimedia Learning”.....................................67 10.11 “The Modern Practice of Adult Education”......................................................67 11 Appendix A: Een inleiding in de Kunstmatige Intelligentie door filosoof Jack Copeland..........................................................................................................................68 11.1 What is Intelligence?...........................................................................................68 11.2 Strong AI, Applied AI, and CS...........................................................................70 11.3 Alan Turing and the Origins of AI......................................................................71 11.4 Early AI Programs..............................................................................................72 11.5 AI Programming Languages...............................................................................75 11.6 Micro-World AI..................................................................................................75 11.7 Expert Systems....................................................................................................77 11.8 The CYC Project.................................................................................................79 11.9 Top-Down AI vs Bottom-Up AI.........................................................................80 11.10 Connectionism..................................................................................................80



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11.10.1 History of Connectionism.........................................................................83 11.11 Nouvelle AI ......................................................................................................86 11.12 Chess.................................................................................................................88 11.13 Is Strong AI Possible?.......................................................................................89 11.13.1 The Chinese Room Objection...................................................................90



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2

Algemene Informatie en Overzicht

2.1 Kruisverwijzingen naar delen in dit document en links naar Web pagina's Opmerking: gebruik Adobe Acrobat Reader 7.0.5 of hoger. Je kan die gratis downloaden via: http://www.adobe.com/nl/products/acrobat/readstep2_allversions.html In een .pdf document dat in het programma Acrobat Reader of in de browser geopend is, kan op de 'kruisverwijzingen' naar andere delen in het document zelf en de 'Web links' naar pagina’s van Websites geklikt worden. Voor 'kruisverwijzingen' wordt in dit document een cursief lettertype gebruikt. 'Web links' naar Web pagina's zijn blauw en onderlijnd. Om op een 'kruisverwijzing' of 'Web link' te klikken: ga met de muisaanwijzer over de tekst van een 'kruisverwijzing' of 'Web link' heen: er verschijnt een handje met omhoog wijzende vinger dat aanduidt dat je de verwijzing kan volgen door met de linker muisknop te klikken. Voor 'Web links' wordt zo nodig je browser gestart en wordt de pagina ‘bezocht’. Voor 'kruisverwijzingen': om terug te keren naar de plaats in het document waar je op een 'kruisverwijzing' hebt geklikt: ●

In Acrobat Reader 7: Gebruik de groene pijl die naar links wijst onderaan het Acrobat Reader venster.

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Vanaf Acrobat Reader 8: Gebruik de zwarte pijl die naar links wijst in een blauwe cirkel bovenaan het venster. Je kan ook de toetsencombinatie Alt-Linkse pijl gebruiken om terug te keren. Als je Acrobat Reader 8 voor de eerste keer start wordt deze zwarte pijl in een blauwe cirkel niet getoond. Door met de rechter muisknop te klikken op één van de icoontjes op de bovenste balk die voor navigatie gebruikt worden, zoals bij voorbeeld één van de grote blauwe pijlen, verschijnt er een menu. Dit menu laat je toe om aan te duiden dat de zwarte pijl om terug te keren naar de vorige “view” (niet naar de vorige bladzijde) moet aanwezig zijn in de bovenste balk.

Aan de linkerkant van het Acrobat Reader 7 venster kan je klikken op de bovenste verticale “tab” om de 'klikbare' inhoudstafel van het document te openen of te sluiten. In Acrobat Reader 8 is er een icoontje in plaats van een “tab”. Door te klikken op een item in deze inhoudstafel kan je naar het overeenkomend hoofdstuk of hoofdstuk deel gaan.



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2.2 Overzicht Het Preventiespel: ●

Als er al over preventie van middelen misbruik en alcohol misbruik door de kinderen gepraat wordt, dan verstaat men daaronder campagnes gericht naar de kinderen via de scholen of de media;

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“Steeds meer ouders verwachten dat het onderwijs hun kinderen opvoedt” [“Laat ze niet schieten! - Geef de grens een plaats in het leven van jongeren” pagina 8 in dat boek] en dat is niet de bedoeling;

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Initiatieven voor de ouders moeten rekening houden met de beproefde basisprincipes betreffende het leren door volwassenen (“Andragogie” van M.S. Knowles)

Daarom ontwierp ik het “Preventiespel”, een educatief spel voor de ouders om ze te laten nadenken over hoe best hun kinderen op te voeden om het risico op middelen of alcohol misbruik door de kinderen zo laag mogelijk te houden. Het gaat hier over: ●

Ook preventie in de schoot van het gezin;

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Zonder saaie 'ex cathedra' lessen;

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Via een spel waarin de ouders problemen krijgen voorgeschoteld aan de hand van cases. Voor elke case krijgen ze een aantal vertrekpunten voor een groepsdiscussie over de case. ●

En dus via 'probleem-gericht' leren: ●

De ouders moeten geen 'uiteenzetting' aanhoren of lezen waarin ze direct een oplossing horen uitspreken of een oplossing lezen, maar moeten zelf nadenken en met elkaar van gedachten wisselen.

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Dit is de beste manier van leren voor volwassenen (volgens de “Andragogie” van M.S. Knowles)

Dit document is bedoeld voor de ontwerpers van de verschillende versies van het Preventiespel, niet voor spelers. In dit document vind je: ●

Het leren en het e-leren: de rol van theorie

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Het leren in groepen

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Het leren door volwassenen



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) ●

Het leren aan de hand van educatieve spellen, simulaties en micro werelden.

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De ontstaansgeschiedenis van het Preventiespel

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De 'Papier en Karton' Kaartspel Versie: het Preventiespel als 'papier en karton' kaartspel, waarbij de spelers een aantal cases moet oplossen via groepsdiscussies. Waarom deze versie en een overzicht van wat.

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De Online Kaartspel Versie: het Preventiespel als online spel, nog uitsluitend gebaseerd op het papieren kaartspel. Waarom deze versie en een overzicht van wat.

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De 'Virtueel Kind' Online Versie: het Preventiespel als online spel, maar nu met een 'virtueel kind' voor elke speler, die verantwoordelijk is voor de opvoeding van zijn/haar 'virtuele kind'. Er is geen kaartspel meer. Tijdens het spel moet de speler een reeks van cases oplossen die afhankelijk zijn van de gekozen persoonlijkheid van zijn 'virtuele kind' en de reactie van de speler op vorige cases. Spelers kunnen met elkaar discuteren over de opvoeding van hun respectievelijke virtuele kinderen. Waarom deze versie en een overzicht van wat.

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De 'Luchtkasteel'? 'Virtueel Kind' Online Versie: het Preventiespel als online spel, met een 'virtueel kind' voor elke speler, die verantwoordelijk is voor de opvoeding van zijn/haar 'virtuele kind'. Er zijn nu geen aparte, wel omlijnde cases meer, maar een 'belevenis' van het leven met het 'virtuele kind' tijdens het spel, waarvan uiteraard de sessies kunnen gespeeld worden gespreid over een lange periode. Waarom deze versie - is ze wel te realiseren met de huidige “state of the art”? - alsook een overzicht van wat.

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Hoe het spel gebruiken in het kader van het grotere geheel van de preventie.


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2.3 Versies van dit document Versies waarvan de nummering begint met 0 zijn klad versies. Versie 0.1

Gepubliceerd ?

Commentaar Om tijd te winnen werden de citaten uit boeken letterlijk overgenomen, dus in het Engels. Het is de bedoeling om later deze inhoud met mijn eigen bewoordingen in het Nederlands te voorzien. Noot: de versie informatie voor klad versies wordt niet behouden in de versie info voor latere versies.

0.2

?

Duidelijk maken in dit document zelf dat het bedoeld is voor ontwerpers, niet voor spelers.

0.3

31-03-12

(1) Wijzigingen vanwege de 'copernicaanse omwenteling': de 'papier & karton' versie is nu een kaartspel i.p.v. een bordspel. (2) Duidelijker maken dat groepsdiscussies tussen spelers het leren sterk bevorderen.

0.4

30-04-12

(1) Sommige opeenvolgende woorden kunnen in het Nederlands aaneen geschreven worden, zoals bijvoorbeeld Preventiespel. (2) Ik heb de korte omschrijving van de 'luchtkasteel?' versie eenvoudiger gemaakt en niet meer verwijzend naar één enkel type systeem uit de Kunstmatige Intelligentie wereld. (3) Tweede 'copernicaanse omwenteling': het preventiespel is nu bedoeld om: (3.1) voor een deel feiten aan te leren (“De effecten van drug D op een mens zijn...”, “De betekenis van 'tolerantie' in de context van drugs is...”) (3.2) en voor het andere deel cases aan te reiken in verband met een aantal thema's en dat voor een bepaalde fase in de ontwikkeling van een kind. Om dan een groepsdiscussie op gang te brengen over een case worden er een aantal mogelijke reacties op de case aangereikt als vertrekpunten.

0.5

(1) Geen 'cases ketting' meer, een stappenplan wordt in 1 aangereikte mogelijke reactie beschreven (als gevolg van de tweede 'copernicaanse omwenteling').


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(2) Aantal mogelijke 'spelers' is nu 2..4: voor elke case is er 1 'speler' van de 2..4 'aan de beurt' en moderator van de groepsdiscussie en zijn er 1..3 andere. Noot: een 'speler' kan een koppel zijn dat samen speelt als een 'team', dus het maximum is 8 personen. 0.6

(1) Invoeren van een nieuwe soort case, nu een “Stappenplan Case” genoemd, die uit een aantal cases bestaat die in een bepaalde volgorde de ene vlak na de andere gespeeld worden. Dit is beter dan (1) in versie 0.5 (2) Geen info kaarten meer, maar een extra thema, thema 8: “Communicatie”.

0.7

(1) Voor de 'papier en karton' versie zijn er nu slechts 7 thema's in plaats van 8, en de namen werden gewijzigd. Dit is nodig: (a) om beter op te lijnen met de inhoud van de boeken van Prof. Dr. Peter Adriaenssens (b) om de nummering van de thema's 2 tot en met 7 te laten overeenkomen met de volgorde waarin de thema's aan bod moeten komen tijdens het spelen van een fase. Thema 1: Leer je kinderen feiten aangaande Drugs, Tabak en Alcohol en de gevolgen van het gebruik ervan. Thema 2: Evolutie van kinderen. Thema 3: Grenzen en normen en hun evolutie. Thema 4: Ken je kinderen zeer goed. Thema 5: Zorg voor het sociale bindweefsel in je gezin. Thema 6: Communicatie. Thema 7: Reacties als een ouder ontdekt dat één/meerdere van zijn/haar kinderen drugs of tabak of overmatig alcohol gebruiken. Er zijn thema's die niet in alle fazen voorkomen. (2) In de 'papier en karton' versie en de daarop gebaseerde online versie worden de case kaarten voor Thema 1 (Kennis over Drugs, Tabak, Alcohol) allemaal gespeeld als eender welke fase gespeeld wordt. Dit om ervoor te zorgen dat de feiten aangaande Drugs, Tabak en Alcohol goed geleerd worden.



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(3) In de 'papier en karton' versie en de daarop gebaseerde online versie: Omdat de case kaarten voor Thema 6 (Communicatie) wat betreft adolescenten niet specifiek zijn voor één bepaalde fase voor adolescenten zullen al de case kaarten voor communicatie met adolescenten gespeeld worden telkens men een fase uit de lijst (fase 4, fase 5, fase 6, fase 7, fase 8) speelt. (4) In de 'papier en karton' versie en de daarop gebaseerde online versie kunnen de spelers kiezen uit het spelen van één of meerdere fase(n) of het spelen van één of meerdere thema('s). Zo kunnen spelers die enkel hun kennis over drugs, tabak en alcohol willen vergroten zich beperken tot het spelen van de kaarten van thema 1.



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3

Het leren en e-leren, door volwassenen, en met educatieve spellen, simulaties en microwerelden

3.1 Introductie In dit hoofdstuk geef ik informatie over onderzoek en theorie over het leren en e-leren, het leren in groep, het leren door volwassenen, en het leren aan de hand van educatieve spellen, simulaties en microwerelden zoals ik ze gevonden heb in boeken, documenten beschikbaar op het Web en Web pagina's. Definities van gebruikte termen zijn natuurlijk belangrijk. Ik vermeld ze ofwel in het deel van een bepaald auteur ofwel in deze introductie als het gaat om termen die door de auteurs als 'gekend' worden beschouwd. Tussen [ en ] zet ik mijn opmerkingen en bijvoegsels ter verduidelijking.

3.1.1 Definities die niet door de beschouwde auteurs gegeven worden

3.1.1.1 “Associationism” A label usually attached to a philosophical/psychological doctrine that asserts that higher-order mental or behavioral processes result from the combination (association) of simpler mental or behavioral elements. Uit “Dictionary of Psychology”

3.1.1.2 “Behaviorism” That approach to psychology which argues that the only appropriate subject matter for scientific psychological investigation is observable, measurable behavior. Uit “Dictionary of Psychology”

3.1.1.3 “Connectionism” In cognitive science, an approach to the study of cognitive processes based on the assumption that a system (such as a brain) operates as though it were composed of a network of nodes, each of which will have at any point in time a certain level of activation. Each of these nodes is assumed to be interconnected with other nodes at different levels of the system in either an excitatory or an inhibitory manner, so that activating one node will have particular effects on the others. In computer science such a set of nodes is called a “neural network”.



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Uit “Dictionary of Psychology”

3.1.1.4 “Constructivism” In perception, a general theoretical position that characterizes perception and perceptual experience as being constructed from, in Gregory's words, 'fleeting fragmentary scraps of data signaled by the senses and drawn from the brain's memory banks – themselves constructions from snippets of the past'. Uit “Dictionary of Psychology”

3.1.1.5 “Explicit learning” Learning that takes place consciously and results in knowledge that is available to consciousness; learning of which one is aware. Uit “Dictionary of Psychology”

3.1.1.6 “Implicit learning” A term coined by A.S. Reber for learning that takes place largely independent of awareness of both the process of acquisition and the content of the knowledge so acquired... ...The classic examples are the acquisition of language and the process of socialization: individuals come to speak their natural language and become inculcated with their society's norms and mores but without conscious knowledge of the underlying principles that guide their behavior. Uit “Dictionary of Psychology”

3.1.1.7 “Instruction” Directing, teaching, imparting knowledge Uit “Dictionary of Psychology”

3.2 T. Mayes en S. de Freitas: Leren en e-Leren, de rol van theorie 3.2.1 [T. Mayes en S. de Freitas] Introductie In Deel I, Hoofdstuk 1 van “Rethinking Pedagogy for a digital age” pagina 13..25 geven zij een overzicht van de theorieën over leren in de Pedagogie en wat die betekenen voor e-Leren. Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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Voor een lijst van door de auteurs geciteerde bronnen zie [T. Mayes en S. de Freitas] Geciteerde bronnen pagina 18. In hun introductie in Deel 1, Hoofdstuk 1 van “Rethinking Pedagogy for a digital age” schrijven T. Mayes en S. de Freitas o.a. het volgende: It is arguable that there are really no models of e-learning per se – only e-enhancements of existing models of learning. Technology can play an important role in the achievement of learning outcomes but it is not necessary to explain this enhancement with a special account of learning. Rather, the challenge is to describe how the technology allows underlying processes common to all learning to function effectively. A true model of e-learning would need to demonstrate on what new learning principles the added value of the 'e' was operating...

3.2.2 [T. Mayes en S. de Freitas] The need for theory In Deel 1, Hoofdstuk 1 van “Rethinking Pedagogy for a digital age” pagina 14 schrijven T. Mayes en S. de Freitas o.a. het volgende: [Biggs, J. (1999)] describes the task of good pedagogical design as one of ensuring that there are absolutely no inconsistencies between the curriculum we teach, the teaching methods we use, the learning environment we choose, and the assessment procedures we adopt. To achieve complete consistency, we need to examine very carefully what assumptions we are making at each stage and to align those. Thus, we need to start with carefully defined intended learning outcomes, we then need to chose learning and teaching activities that stand a good chance of allowing the students to achieve that learning, then we need to design assessment tasks that will genuinely test whether the outcomes have been reached... ...The main purpose of this chapter is to outline the theoretical underpinning of e-learning, and to argue that, to be comprehensive, e-learning design must consider three fundamental perspectives, each of which leads to a particular view of what matters in pedagogy. The intention is to show how e-learning can be approached in a principled way, which means uncovering the implicit assumptions about e-pedagogy, and then asking the right questions...

3.2.3 [T. Mayes en S. de Freitas] Learning theory and pedagogical design In Deel 1, Hoofdstuk 1 van “Rethinking Pedagogy for a digital age” pagina 14..15 schrijven T. Mayes en S. de Freitas o.a. het volgende:



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There are distinct traditions in educational theory that derive from different perspectives about the nature of learning itself. Although learning theory is often presented as though there is a large set of competing accounts for the same phenomena, it is more accurate to think of theory as a set of quite compatible explanations for a large range of different phenomena. In fact it is probably true to say that never before has there been such agreement about the psychological fundamentals [Jonassen, D.H. and Land, S.M. (2000)]. Here, we follow the approach of [Greeno, J.G., Collins, A.M. and Resnick, L. (1996)] in identifying three clusters or broad perspectives that make fundamentally different assumptions about what is being explained. [maar deze veronderstellingen zijn compatibel]

3.2.3.1 [T. Mayes en S. de Freitas] The associationist perspective In Deel 1, Hoofdstuk 1 van “Rethinking Pedagogy for a digital age” pagina 15..16 schrijven T. Mayes en S. de Freitas o.a. het volgende: The associationist approach models learning as the gradual building of patterns of associations and skill components. Learning occurs through the process of connecting the elementary mental or behavioural units, through sequences of activity followed by feedback. This view encompasses the research traditions of associationism [“Associationism”], behaviorism [“Behaviorism”] and connectionism (neural networks) [“Connectionism”]... ...[Gagné, R. (1985)] set out the psychological principles on which the dominant approach to training has subsequently been based. The instructional approach known as Instructional Systems Design (ISD) is essentially a recursive decomposition of knowledge and skill. Much of what is termed e-learning is still based in the training departments of organizations within a training philosophy that is traditional ISD. The intellectual base for this consists of principles that are widely accepted within the organizational training culture and which derive essentially from associationism [“Associationism”]...

3.2.3.2 [T. Mayes en S. de Freitas] The cognitive perspective In Deel 1, Hoofdstuk 1 van “Rethinking Pedagogy for a digital age” pagina 16..18 schrijven T. Mayes en S. de Freitas o.a. het volgende: As part of a general shift in theoretical positioning in psychology starting in the 1960s, learning, as well as perception, thinking, language and reasoning became seen as the output of an individual's attention, memory and concept formation processes... ...Knowledge acquisition was viewed as the outcome of an interaction between new experiences and the structures for understanding that have already been created...



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...In school-level educational research the influence of Piaget has been very significant, in particular his assumption that conceptual development occurs through intellectual activity rather than by the absorption of information. Piaget's constructivist [“Constructivism”] theory of knowledge [Piaget, J. (1970)] was based on the assumption that learners do not copy or absorb ideas from the external world, but must construct their concepts through active end personal experimentation and observation... ...The emphasis on task-based learning and reflection can be seen as a reaction to the rapid development of multimedia and hypermedia in the 1980s and early 1990s, in which a tendency for technology-based practice to resurrect traditional instructionist [“Instruction”] approaches was evident...

3.2.3.3 [T. Mayes en S. de Freitas] The situative perspective In Deel 1, Hoofdstuk 1 van “Rethinking Pedagogy for a digital age” pagina 18..20 schrijven T. Mayes en S. de Freitas o.a. het volgende: The social perspective on learning has received a major boost from the gradual reconceptualization of all learning as 'situated'. A learner will always be subjected to influences from the social and cultural setting in which the learning occurs, which will also, at least partly, define the learning outcomes. This view of learning focuses on the way knowledge is distributed socially. When knowledge is seen as situated in the practices of communities then the outcomes of learning involve the abilities of individuals to participate in those practices successfully. The focus shifts right away from analyses of components of subtasks, and onto the patterns of successful practice. This can be seen as a necessary correction to theories of learning in which both the behavioral and cognitive levels of analysis had become disconnected from the social...

3.2.4 [T. Mayes en S. de Freitas] E-learning and the learning cycle In Deel 1, Hoofdstuk 1 van “Rethinking Pedagogy for a digital age” pagina 20..22 schrijven T. Mayes en S. de Freitas o.a. het volgende: It is possible to view these differing perspectives as analyzing learning at different levels of aggregation. An associationist analysis describes the overt activities, and the outcomes of these activities, for individual learners. A cognitive analysis attempts a level of analysis that describes the detailed structures and processes that underlie individual performance. The situative perspective perspective aggregates at the level of groups of learners,



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describing activity systems in which individuals participate as members of communities. There will be few current examples of approaches that derive from taking just one level of analysis and neglecting the others. Most implementations of e-learning will include blended elements that emphasize all three levels: learning as behavior, learning as the construction of knowledge and meaning, and learning as social practice... ...There is quite a long tradition of describing learning as a cycle through stages, with each cycle focusing in turn on different perspectives [Fitts, P. and Posner, M.I. (1967); Rummelhart, D.E. and Norman, D.A. (1978); Kolb, D.A. (1984); Mayes, J.T. and Fowler, C.J.H. (1999)]... ...Depicting our three perspectives as a cycle invites the e-learning designer to consider what kind of technology is most effective at what stage of learning. [Fowler, C.J.H. and Mayes, J.T. (1999)] attempted to map broad pedagogies onto types of technology, distinguishing between the technology of presenting information (primary), the technology of supporting active learning tasks and feedback (secondary), and the technology of supporting dialog about the application of the new learning (tertiary)... ...When we consider the current landscape of e-learning another kind of model suggests itself, based perhaps on a simple dimension of locus of control. At one end of this dimension we have institutional virtual learning environments (VLEs), with their emphasis on standardization. These are at the institution-incontrol end of this dimension. At the other end is an environment that empowers learners to take responsibility for their own learning to the point where they make their own design decisions... ...We might bring these ideas together in the following way. The stages represent a cycle that starts with the social. Motivation to start and continue learning will be derived from communities and peers. This represents the situative perspective and it is served by the various technologies that allow the identification of, and communication with, others who will share in, or contribute to in some way, the learning experience. Gradually, personal ownership of the learning activities becomes necessary for the derivation of meaning and the construction of understanding. Learning tasks come into play. These will involve the production of outputs that can only be achieved through understanding. This brings the cognitive perspective into focus. The learner will interact with subject matter, but in a way that manipulates it actively... ...however, it will at times be necessary to subject oneself as a learner to the discipline of bottom-up mastery of the components of a task, so an associationist perspective will underpin pedagogy at key moments. As learning progresses, so the learner will benefit from checking progress with



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peers, and engaging in dialog about the refinements of the developing understanding, and the associated skills, so the cycle can continue for as long as necessary.

3.2.5 [T. Mayes en S. de Freitas] Geciteerde bronnen Biggs, J. (1999) Teaching for Quality Learning at University, Buckingham: Society for Research in Higher Education and Open University Press. Fitts, P. and Posner, M.I. (1967) Human Performance, Monterey, CA: Brooks/Cole. Fowler, C.J.H. and Mayes, J.T. (1999) 'Learning relationships from theory to design', Association for Learning Technology Journal, 7 (3): 6-16. Gagné, R. (1985) The Conditions of Learning, New York: Holt, Rinehart & Winston. Greeno, J.G., Collins, A.M. and Resnick, L. (1996) 'Cognition and learning', in D.C. Berliner and R.C. Calfee (eds) Handbook of Educational Psychology, New York: Simon & Schuster/Macmillan. Jonassen, D.H. and Land, S.M. (2000) Theoretical Foundations of Learning Environments, Mahwah, NJ: Lawrence Erlbaum. Kolb, D.A. (1984) Experiential Learning: Experience as the Source of Learning and Development, Englewood Cliffs, NJ: Prentice-Hall. Mayes, J.T. and Fowler, C.J.H. (1999) 'Learning technology and usability: a framework for understanding courseware', Interacting with Computers, 11: 485-97. Piaget, J. (1970) Science of Education and the Psychology of the Child, New York: Orion Press. Rummelhart, D.E. and Norman, D.A. (1978) 'Accretion, tuning and structuring: three modes of learning', in J.W. Cotton and R.L. Klatzky (eds) Semantic Factors in Cognition, Hillsdale, NJ: Erlbaum.

3.3 D. Jaques en G. Salmon: Leren in groepen 3.3.1 [D. Jaques en G. Salmon] Introductie In de introductie in het boek “Learning in Groups - A Handbook for face-to-face and online environments” , pagina 1..4, zeggen de auteurs o.a. het volgende: (1) [Group dynamics:] Group interaction: Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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Allows students to negotiate meanings.

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Allows students to express themselves in the language of the subject.

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Allows students to establish a more intimate and dialectical contact with academic and teaching staff than more formal methods permit.

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Develops the more instrumental skills of listening, careful reading, presenting ideas, persuading and teamwork [en dat zijn vaardigheden die ouders nodig hebben voor de communicatie met hun kinderen].

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Gives students the chance to monitor their own learning, and thus gain a degree of self-direction [zie in het deel over leren door volwassenen [M.S. Knowles] The learners' self-concept].

(2) [Group dynamics:] In all human interactions there are three main ingredients: ●

Content: relates to the subject matter or task on which people are working.

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Process: refers to the dynamics (both emotional, intellectual and behavioural) of what is happening between those involved.

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Structure: When a group comes together for the first time and begins to interact, various differences between the members begin to appear: differences in status, influence, role, ability, and so on. The pattern of relationships that is thus established is known as the group structure.[dit staat pas op pagina 31 in hun boek]

(3) [Group dynamics:] Cooperation is a key word in learning groups: ●

Tutors need to develop a clear and coordinated strategy for teaching students how to work together and improve their cooperative skills.

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Cooperation also means each and every member of the group taking part and sharing responsibility for its success.

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And in some cases [it also means] having a clear brief to support each other's learning.

In sum, an effective group will have both common shared aims and differentiated individual aims. (4) [Group dynamics:] Assessment: Assessment too has an important part to play in drawing the attention of students to the importance of effective group work and their part in it and, where it is accompanied by self and peer assessment and team grades, provides strong motivation to take full part in, and learn about, peer learning and teamwork.



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3.3.2 [D. Jaques en G. Salmon] Groepen en Teams In hoofdstuk 1 in het boek “Learning in Groups - A Handbook for face-to-face and online environments” , pagina 6, zeggen de auteurs o.a. het volgende:

3.3.2.1 Groepen Groups exist as more than a collection of people when they possess all of the following qualities to a greater or lesser degree: ●

Collective perception: members are collectively conscious of their existence as a group.

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Needs: people join a group because they believe it will satisfy some needs or give them some rewards such as recognition or self understanding through feedback.

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Shared aims: members hold or quickly develop some shared aims or ideals which bind them together. The achievement of aims is maybe one of the rewards.

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Interdependence: members are interdependent insofar as they are affected by and respond to any event that affects any of the group's members.

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Social organization: it comprises a social unit with norms, roles, statuses, power and emotional relationships.

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Interaction: members influence and respond to each other in the process of communicating. The sense of group exists even when members are not collected in the same place, such as when they are part of an online group.

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Cohesiveness: members want to remain in the group, to contribute to its well-being and aims, and to join in its activities.

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Membership: two or more people interacting explicitly or implicitly for longer than a few minutes constitute a group if there is recognition of mutual bonds.

3.3.2.2 Teams We use the term group for people who come together to share knowledge, for personal development or to learn from each other through discussion. We use team for groups that are engaged in a task or project geared towards an end product or decision. All teams can thus be viewed as groups, but not all groups as teams.

3.3.3 [D. Jaques en G. Salmon] The “Tavistock” model



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In hoofdstuk 1 in het boek “Learning in Groups - A Handbook for face-to-face and online environments” , pagina 8..11, zeggen de auteurs o.a. het volgende: The Tavistock theoretical concepts were developed through a wealth of experience of groups in therapeutic settings and at conferences in human relations training and the Tavistock Institute applied them with some degree of success to industrial and organizational settings... ...They may be less relevant to learning groups committed to the task of intellectual development, but we think that every tutor should at least be aware of them as a latent force, as when issues surface they can be quite powerful. Authority in groups: Whenever decisions have to be made, as in a team or project group, about tasks and process or about the allocation rules, authority problems are likely to occur. Whose job is it to decide? Can the group give any one person that sanction? Where a designated 'authority person' exists, a group may either find itself dependent on them, or counter-dependent (attacking authority)... ...Many students who object to the authority of the teacher are not really seeking an alternative to the status quo. They may be avoiding the need to accept that learning is their own responsibility and that they have to face the consequences of the choices they make. It is important therefore for the teacher to create the conditions in which the students can make conscious choices of alternative courses of action, supportively but firmly bringing such issues out into the open. Responsibility: Often there is a feeling in groups that the ultimate responsibility for each person's action, and its consequences, resides in any figure of authority... ...It may seem easier to respond to a student's sense of helplessness by offering to meet it and without questioning its provenance, to play the role of the compulsive helper, thus missing an opportunity to develop the student's capacity for self-growth into greater autonomy and responsibility. Boundaries: All of us have a physical and psychological boundary in relation to others... ...The same can be said of the group; both in a subjective sense, and a more objective and symbolic way, boundaries can distinguish one group from another... ...Less tangible and more subjective are the task boundary, which determines what the group should or should not do, and the input boundary which requires members to undergo certain social procedures before membership is acquired. Projection: Sometimes the negative feelings we have towards other people are too dangerous to permit of conscious expression and, as a mechanism to defend us against the anxiety that this produces, we attribute these feelings, motives or qualities to the person or persons [bijvoorbeeld een andere groep] towards whom our feelings are really directed. We thus experience the feelings as coming 'at us' rather than 'from us'. Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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Organizational structure: The power relationships in the group, whether determined by outside factors (e.g. the course, the tutor's position in the institutional hierarchy) or by internal concerns such as how clever or dominating other students are or appear to be, can have a profound effect on the work of a group. Structural relations of this kind may manifest themselves in who sits where, who takes initiatives, who defers to whom, and in the 'pecking order' of contributions... ...The recognition of this problem had led some group leaders to allocate special roles and responsibilities in a group on a rotating basis. Large groups: As the size of a group increases, so its characteristics change... ...Many regard 6 as a critical number for groups in all sorts of situations. With 6 or less, the degree of intimacy, whether physical or virtual, offered by close proximity can somehow make it difficult for group members to register their feelings about the group. Leadership tends to be fluid and interchangeable. As the group size increases, the climate of the group changes. Individuals become less constrained by the norms of the group and become aware of their feelings. In addition, leadership and other roles become more established. With numbers of 12 to 25 the likelihood of full face-to-face – indeed virtual – interaction decreases and sub-groups start to emerge. When the group is over 25 in number, effective interaction between everyone becomes almost impossible.

3.3.4 [D. Jaques en G. Salmon] Group analysis In hoofdstuk 1 in het boek “Learning in Groups - A Handbook for face-to-face and online environments” , pagina 11..12, zeggen de auteurs o.a. het volgende: Group analysis provides a more social view of interaction which it sees as taking place at four levels: ●

Mirroring implies a degree of sameness between members

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Exchange arises through difference

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Social Integration: the social nature of group members enables a network or matrix of discussion where each person can compare and contrast their own ways of thinking and behaving with that of others and to change these as he or she thinks fit. Because perceptions are often based on assumptions which are non-rational and even unconscious, this process is capable of bringing to the surface a range of otherwise untested aspects of both personal and shared learning.



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Collective unconscious: each group will naturally, through a process of 'other-than-conscious' trial and error, sort out its social rules and thus acquire its distinctive culture.

3.4 M.S. Knowles (1913-1997) over het leren door volwassenen 3.4.1 [M.S. Knowles] Introductie Malcolm S. Knowles is de vader van de “Andragogie”, de wetenschap van het leren door volwassenen. Hij bedacht deze naam in analogie met “Pedagogie”, betreffende kinderen en adolescenten. In zijn boeken “The Modern Practice of Adult Education” en “The Adult Learner” brengt hij de resultaten van onderzoek alsook zijn aanbevelingen voor organisaties die te maken hebben met het leren door volwassenen. Voor een lijst van door de auteur geciteerde bronnen zie [M.S. Knowles] Geciteerde bronnen pagina 33.

3.4.2 [M.S. Knowles] The Modern Practice of Adult Education M.S. Knowles geeft in zijn boek “The Modern Practice of Adult Education” een overzicht van de voorname aspecten van het leren door volwassenen:

In a world of accelerating change ...Adult educators who used the practices in the seventies that they had learned in the sixties were ineffective and archaic. “Modern” is thus a very temporary state. My own assessment is that the half-life of current practices is about a decade – that half of the practices become outdated over the course of ten years. So about half of this book had to be rewritten when I revised the 1970 edition. [dit werd in 1980 geschreven] One of the reassuring features of this process of change, however, is that the basic concepts and assumptions about adult learners and adult learning that have been flowing through our stream of thought for half a century have remained intact...

A new conception of the purpose of education ...The faith has been that if we simply pour enough knowledge into people: (1) they will turn out to be good people, and (2) they will know how to make use of their knowledge... ...But in an era of knowledge explosion, technological revolution... ...this definition of the purpose of education and this faith in the power of



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transmitted knowledge are no longer appropriate. We now know that in the world of the future we must define the mission of education as to produce competent people – people who are able to apply their knowledge under changing conditions; and we know that the foundational competence all people must have is the competence to engage in lifelong self-directed learning...

From a focus on teaching to a focus on learning ...Until quite recently most educational psychologists (with the exception of Piaget and Bruner) gave their attention almost exclusively to studying the reactions of children to teaching, and schools of education gave their attention primarily to training teachers how to control students' reactions to their teaching. With Piaget's and Bruner's discoveries that children have a natural ability to conceptualize and Tough's finding that adults go through a natural sequence of steps when they undertake to learn something on their own, we began to be interested in finding out more about the natural process of learning – focusing on what happens inside the learner rather than on what the teacher does. Out of this line of thinking came a new emphasis on education as a process of facilitating self-directed learning and a redefinition of the role of the teacher as a facilitator of self-directed learning and a resource to self-directed learners.

Lifelong learning ...in a world of accelerating change learning must be a lifelong process. Therefore, schooling must be primarily concerned with providing the resources and support for self-directed inquirers.

New delivery systems ...a concern for developing new ways to deliver educational services to individuals so that they can go on learning throughout their lives at their convenience in terms of time and place... ...We now perceive that resources for learning are everywhere in our environment and that people can get help in their learning from a variety of other people. The modern task of education, therefore, becomes one of finding new ways to link learners with learning resources.

3.4.3 [M.S. Knowles] Een theorie betreffende het leren door volwassenen: Andragogie en het andragogisch Model (Theory of learning) Zie “The Adult Learner” pagina's 64..70 in dat boek. Het andragogisch model is gebaseerd op het volgende, zoals M.S. Knowles het verwoordt:



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3.4.3.1 [M.S. Knowles] The need to know Adults need to know why they need to learn something before undertaking to learn it... ...the first task of the facilitator of learning is to help the learners become aware of the “need to know”. At the very least, facilitators can make an intellectual case for the value of the learning in improving the effectiveness of the learners' performance or the quality of their lives. Even more potent tools for raising the level of awareness of the need to know are real or simulated experiences in which the learners discover for themselves the gaps between where they are now and where they want to be.

3.4.3.2 [M.S. Knowles] The learners' self-concept Adults have a self-concept of being responsible for their own decisions, for their own lives. Once they have arrived at that self-concept they develop a deep psychological need to be seen by others and treated by others as being capable of self-direction. They resent and resist situations in which they feel others are imposing their wills on them... ...As adult educators become aware of this problem, they make efforts to create learning experiences in which adults are helped to make the transition from dependent to self-directing learners.

3.4.3.3 [M.S. Knowles] The role of the learners' experiences Adults come into an educational activity with both a greater volume and a different quality of experience from youths... ...Any group of adults will be more heterogeneous in terms of background, learning style, motivation, needs, interests, and goals than is true for a group of youths. Hence, greater emphasis in adult education is placed on individualization of teaching and learning strategies. It also means that for many kinds of learning, the richest resources for learning reside in the adult learners themselves. Hence, the emphasis in adult education is on experiential techniques – techniques that tap into the experience of the learners, such as group discussion, simulation exercises, problem-solving activities, case method, and laboratory methods instead of transmittal techniques. Also, greater emphasis is placed on peer-helping activities. But the fact of greater experience also has some potentially negative effects. As we accumulate experience, we tend to develop mental habits, biases, and presuppositions that tend to cause us to close our minds to new ideas, fresh Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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perceptions, and alternative ways of thinking. Accordingly, adult educators try to discover ways to help adults examine their habits and biases and open their minds to new approaches... ...There is another, more subtle reason for emphasizing the experience of the learners; it has to do with the learner's self-identity... ...As they mature, they increasingly define themselves in terms of the experiences they have had... ...to adults, their experience is who they are. The implication of this fact for adult education is that in any situation in which the participants' experiences are ignored or devalued, adults will perceive this as rejecting not only their experience, but rejecting themselves as persons.

3.4.3.4 [M.S. Knowles] Readiness to learn Adults become ready to learn those things they need to know and be able to do in order to cope effectively with their real-life situations... ...For example, a sophomore girl in high school is not ready to learn about infant nutrition or marital relations, but let her get engaged after graduation and she will be very ready.

3.4.3.5 [M.S. Knowles] Orientation to learning In contrast to children's and youth's subject-centered orientation to learning (at least in school), adults are life-centered (or task-centered or problem-centered) in their orientation to learning... ...Furthermore, they learn new knowledge, understandings, skills, values, and attitudes most effectively when they are presented in the context of application to real-life situations.

3.4.3.6 [M.S. Knowles] Motivation While adults are responsive to some external motivators (better jobs, promotions, higher salaries, and the like), the most potent motivators are internal pressures (the desire for increased job satisfaction, self-esteem, quality of life, and the like). [Tough, A. (1979)] found in his research that all normal adults are motivated to keep growing and developing, but this motivation is frequently blocked by such barriers as negative self-concept as a student, inaccessibility of opportunities or resources, time constraints, and programs that violate the principles of adult learning.



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3.4.4 [M.S. Knowles] Theories of teaching Zie “The Adult Learner” pagina 73..113 in dat boek. A distinction must be made between theories of learning and theories of teaching. While theories of learning deal with the ways in which an organism learns, theories of teaching deal with the ways in which a person influences an organism to learn [Gage, N.L. (1972), p. 56]. Ik beschouwen hier verder in detail: ●

Het helpen van volwassenen om te leren

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Leren via onderzoek door de lerende (Teaching through inquiry)

Omdat de termen “teaching” en “teacher” teveel doen denken aan klassiek 'ex cathedra' 'lesgeven' door een 'lesgever' gebruik ik in wat volgt eerder de termen “facilitating” en “facilitator”, namelijk het helpen van mensen om te leren en een 'ding' of persoon die dat doet.

3.4.4.1 [M.S. Knowles] Het helpen van volwassenen om te leren Zie “The Adult Learner” pagina's 84..87 in dat boek. M.S. Knowles citeert de lijst met 10 richtlijnen voor een facilitator opgesteld door C. Rogers: 1. The facilitator has much to do with setting the initial mood or climate of the group or class experience... 2. The facilitator helps to elicit and clarify the purposes of the individuals in the class as well as the more general purposes of the group... 3. He relies upon the desire of each student to implement those purposes which have meaning for him as the motivational force behind significant learning... 4. He endeavors to organize and make easily available the widest possible range of resources for learning... 5. He regards himself as a flexible resource to be used by the group. He does not downgrade himself as a resource. He makes himself available as a counselor, lecturer, and advisor... 6. In responding to expressions in the classroom group, he accepts both intellectual content and the emotionalized attitudes, endeavoring to give each aspect the approximate degree of emphasis which it has for the individual or the group... 7. As the acceptant classroom climate becomes established, the facilitator is able increasingly to become a participant learner, a member of the group, expressing his



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views as those of one individual only. 8. He takes the initiative in sharing himself with the group – his feelings as well as his thoughts – in ways which do not demand or impose but represent simply the personal sharing which students may take or leave... 9. Throughout the classroom experience, he remains alert to the expressions indicative of deep or strong feelings. These may be feelings of conflict, pain, and the like, which exist primarily within the individual. Here he endeavors to understand these from the person's point of view and to communicate his emphatic understanding. On the other hand, the feelings may be those of anger, scorn, affection, rivalry, and the like – interpersonal attitudes among members of the group. Again he is as alert to these as to the ideas being expressed and by his acceptance of such tensions or bonds he helps to bring them into the open for constructive understanding and use by the group. 10. In his functioning as a facilitator of learning, the leader endeavors to recognize and accept his own limitations... ...There will be many times when his attitudes are not facilitative of learning... ...When he experiences attitudes which are nonfacilitative, he will endeavor to get close to them, to be clearly aware of them, and to state them just as they are within himself. Once he has expressed these angers, these judgments, these mistrusts, these doubts of others and doubts of himself, as something coming from within himself, not as objective facts in outward reality, he will find the air clear for a significant interchange between himself and his students.

3.4.4.2 [M.S. Knowles] Leren via onderzoek door de lerende (Teaching [Facilitating] through inquiry) Zie “The Adult Learner” pagina 96..102 in dat boek. “Teaching through inquiry” staat ook bekend als “The discovery method”, “The inquiry method”, “self-directed learning” of “problem-solving learning”. [Postman, N. and Weingartner, C. (1969)] stelden een lijst op van gedragingen die kunnen geobserveerd worden bij een facilitator die de “onderzoek methode” gebruikt: The teacher [facilitator] rarely tells students what [the subject matter] he thinks they ought to know. He believes that telling, when used as a basic teaching strategy, deprives students of the excitement of doing their own finding and of the opportunity for increasing their power as learners.



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His basic mode of discourse with students is questioning. While he uses both convergent and divergent questions, he regards the latter as the more important tool... ...he sees questions as instruments to open engaged minds to unsuspected possibilities. Generally, he does not accept a single statement as an answer to a question. In fact, he has a persisting aversion to anyone, any syllabus, , any text that offers The Right Answer... ...because he knows how often The Right Answer serves only to terminate further thought... ...He knows, too, the power of contingent thinking. He is the most “It depends” learner in his class. He encourages student-student interaction as opposed to student-teacher interaction. And generally he avoids acting as a mediator or judge of the quality of ideas expressed. The inquiry teacher is interested in students developing their own criteria or standards for judging the quality, precision, and relevance of ideas. He rarely summarizes the positions taken by students on the learnings that occur. He recognizes that the act of summary, of “closure”, tends to have the effect of ending further thought... ...If a student has arrived at a particular conclusion, then little is gained by the teacher's restating it. If the student has not arrived at a conclusion, then it is presumptuous and dishonest for the teacher to contend that he has. Any teacher who tells you precisely what his students learned during any lesson, unit, or semester quite literally does not know what he is talking about. His lessons develop from the responses of students and not from a previously determined “logical” structure. The only kind of lesson plan, or syllabus, that makes sense to him is one that tries to predict, account for, and deal with the authentic responses of learners to a particular problem: the kinds of questions they will ask, the obstacles they will face, their attitudes, the possible solutions they will offer, etc... ...In short, the “content” of his lessons are the responses of his students... ...He is engaged in exploring the way students think, not what they should think. Generally, each of his lessons poses a problem for students. Almost all of his questions, proposed activities, and assignments are aimed at having his students clarify a problem, make observations relevant to the solution of the problem, and make generalizations based on their observations... ...Thus, our inquiry, or “inductive” teacher is largely interested in helping his students to become more proficient as users of these methods.

3.4.5 [M.S. Knowles] Aanvaarding van de theorieën en resultaten van onderzoek betreffende de theorieën Zie “The Adult Learner” pagina 133..152



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De 6 kern principes van de andragogie zijn volgens de theorie: ● ● ● ● ● ●

The learner's need to know Self-directed learning Prior experiences of the learner Readiness to learn Orientation to learning: problem solving Motivation to learn

3.4.5.1 [M.S. Knowles] The learner's need to know The core principle that adults “need to know” why before they engage in learning has led to the now generally accepted premise that adults should be engaged in a collaborative planning process for their learning... ...Even in learning situations in which the learning content is prescribed, sharing control over the learning strategies is believed to make learning more effective... Because mutual planning is so widely accepted and generally found to be effective by most practitioners, few researchers have been motivated to test this assumption. Training researchers have conducted research related to this premise that suggests three dimensions to the need to know: the need to know how learning will be conducted, what learning will occur, and why learning is important. Zie voor meer details “The Adult Learner” pagina 133..135.

3.4.5.2 [M.S. Knowles] Self-directed learning Perhaps no aspect of andragogy has received so much attention and debate as the premise that adults are self-directed learners. That adults can and do engage in selfdirected learning (SDL) is now a foregone conclusion in adult learning research. Questions remain as to whether self-directed learning is a characteristic of adult learners, and whether it should be a goal of adult educators to help all adult learners become self-directed... ...There are two conceptions of self-directed learning prevalent in the literature [Brookfield, S.D. (1986); Candy, P.C. (1991)]. ●

First, self-directed learning is seen as self-teaching, whereby learners are capable of taking control of the mechanics and techniques of teaching themselves in a particular subject...

●

...Second, self-directed learning is conceived of as personal autonomy, which [Candy, P.C. (1991)] calls autodidaxy. Autonomy means taking control of the goals and purposes of learning and assuming ownership of learning. This leads to an internal change of consciousness in which the learner sees knowledge as contextual and freely questions what is learned.

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These two dimensions of self-directed learning are relatively independent, though they may overlap. A person may have a high degree of personal



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autonomy, but choose to learn in a highly teacher-directed instructional setting because of convenience, speed, or learning style... ...For most learning professionals, the most important dimension of selfdirected learning is building personal autonomy. The assumption that all adults have full capacity for self-teaching and personal autonomy in every learning situation is generally not accepted. Any particular learner in a particular learning situation is likely to exhibit different capabilities and preferences... ...There are many factors that individuals weigh in choosing whether to behave in a self-directed way at a particular point. These may include: ●

Learning style

●

Previous experience with the subject matter

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Social orientation

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Efficiency

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Previous learning socialization

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Locus of control Those who ascribe control of events to themselves are said to have internal locus of control and are referred to as internals. People who attribute control to outside forces are said to have an external locus of control and are termed externals [Spector, P.A. (1982)]

That an adult learner may choose not to be self-directed, for whatever reason, does not invalidate the core principle that adults, and adults in the United States in particular, have a self-concept of being independent. In fact, it is having the freedom to choose their learning strategy that is critical. It is the sense of personal autonomy, not self-teaching, that seems to be most important to adults. The biggest problems arise when adult learners want to have more independence in their learning but are denied that opportunity... Zie voor meer details “The Adult Learner” pagina 135..139.

3.4.5.3 [M.S. Knowles] Prior experiences of the learner ...there is growing recognition from multiple disciplines that adults' experience has a very important impact on the learning process. While adult leaders have long capitalized on adult learners' experiences as a resource for learning, they have not adequately recognized its role as a gatekeeper for learning. On the one hand, experience can aid in learning new knowledge if the new knowledge is presented in such a way that it can be related to existing knowledge and mental models. On the other hand, those same mental models can become giant barriers to new learning when the new learning challenges them. Thus, the unlearning process becomes as important as the learning process when new learning significantly challenges existing schema. Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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...Three streams of closely related cognitive psychological research help explain how prior experience influences learning: schema theory, information processing, and memory research [Jonassen, D.H. and Grabowski, B.L. (1993)]... Zie voor meer details “The Adult Learner” pagina 139..144.

3.4.5.4 [M.S. Knowles] Readiness to learn Adults generally become ready to learn when their life situation creates a need to know. It then follows that the more adult learning professionals can anticipate and understand adults' life situations and readiness for learning, the more effective they can be. The challenge has been to develop models to explain typical variability in adult readiness to learn. [Pratt, D.D. (1988)] proposed a useful model of how adult's life situations not only affect their readiness to learn, but also their readiness for andragogical-type learning experiences... ...Pratt's model, though untested [in 1998] provides a conceptual explanation for some of the variability that adult learning facilitators encounter in any group of adult learners... Zie voor meer details “The Adult Learner” pagina 144..146.

3.4.5.5 [M.S. Knowles] Orientation to learning: Problem solving ...We [M.S. Knowles et al.] said earlier that adults generally prefer a problem solving orientation to learning, rather than subject-centered learning. Furthermore, they learn best when new information is presented in real-life context. As a result, the experiential approach to learning has become firmly rooted in adult learning practice. [Kolb, D.A. (1984)] has been a leader in advancing the practice of experiential learning. He defines learning as “The process whereby knowledge is created through transformation of experience” (p. 38) [in Kolb's publication]. For Kolb, learning is not so much the acquisition or transmission of content as the interaction between content and experience, whereby each transforms the other. The educator's job, he says, is not only to transmit or implant new ideas, but also to modify old ones that may get in the way of new ones... ...Kolb suggests that there are four steps in the experiential learning cycle: 1. Concrete experience – full involvement in new here-and-now experiences. 2. Observations and reflection – reflection on and observation of the learner's experiences from many perspectives. 3. Formation of abstract concepts and generalization – creation of concepts that integrate the learners' observations into logically sound theories. Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)

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4. Testing implications of new concepts in new situations – using these theories to make decisions and solve problems. [5. Go back to step 1]... ...Kolb's model with suggested learning strategies Kolb's stage

Example strategy

Concrete experience

Simulation, Case study, Field trip, Real experience, Demonstrations

Observe and reflect

Discussion, Small groups, Buzz groups, Designated observers

Abstract conceptualization

Sharing content

Active experimentation

Laboratory experiences, On-the-job experience, Internships, Practice sessions

Research on Kolb's model [up to 1998] has focused mostly on the learning styles he proposed. Unfortunately, research has done little to validate his theory, due in large part to methodological concerns about his instrument [Cornwell, J.M. and Manfredo, P.A. (1994); Freedman, R.D. and Stumpf, S.A. (1980); Kolb, D.A. (1981); Stumpf, S.A. and Freedman, R.D. (1981)]... Zie “The Adult Learner” pagina 146..149.

3.4.5.6 [M.S. Knowles] Motivation to learn The andragogical model of adult learning makes some fundamentally different assumptions about what motivates adults to learn... ...The first principle of andragogy states that “adults need to know why they need to learn something before undertaking to learn it”. Knowing why they need to learn something is the key to giving adults a sense of volition about their learning. Principle 6 states that the most potent motivators for adults are internal ones: for example, quality of life, satisfaction, and self-esteem... ...This does not mean that external payoffs (for example, salary increase) have no relevance, but rather that the internal need satisfaction is the more potent motivator. M.S. Knowles et al. vermelden geen research aangaande de juistheid van deze beweringen. Zie “The Adult Learner” pagina 149..150.

3.4.6 [M.S. Knowles] Geciteerde bronnen



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Brookfield, S.D. (1986) Understanding and Facilitating Adult Learning. San Francisco: Jossey Bass. Candy, P.C. (1991) Self-Direction for Lifelong Learning. San Francisco: Jossey Bass. Cornwell, J.M. and Manfredo, P.A. (1994) “Kolb's Learning Style Theory Revisited.” Educational and Psychological Measurement, 54, 317-327. Freedman, R.D. and Stumpf, S.A. (1980) ”Learning Style Theory: Less Than Meets the Eye.” Academy of Management Review, 5, 445-447. Gage, N.L. (1972) Teacher Effectiveness and Teacher Education. Palo Alto, CA: Pacific Books. Jonassen, D.H. and Grabowski, B.L. (1993) Handbook of Individual Differences, Learning, and Instruction. Hillsdale, NJ: Lawrence Erlbaum. Kolb, D.A. (1981) “Experiential Learning Theory and the Learning Style Inventory: A reply to Freedman and Stumpf.” Academy of Management Review, 6, 289-296. Kolb, D.A. (1984) Experiential Learning: Experience as the Source of Learning and Development, Englewood Cliffs, NJ: Prentice-Hall. Postman, N. and Weingartner, C. (1969) Teaching as a Subversive Activity. New York: Dell. Pratt, D.D. (1988) “Andragogy as a Relational Construct.” Adult Education Quarterly, 38, 160-181. Rogers, C.R. (1969) Freedom to Learn. Columbus, Ohio: Merrill. Spector, P.A. (1982) “Behavior in Organization as a Function of Employee's Locus of Control.” Psychological Bulletin. 91(30), 482-497. Stumpf, S.A. and Freedman, R.D. (1981) “The Learning Style Inventory: Still Less than Meets the Eye.” Academy of Management Review, 6, 297-299. Tough, A. (1979) The Adult's Learning Projects. Toronto: Ontario Institute for Studies in Education.

3.5 L.P. Rieber over Educatieve Spellen, Simulaties, en Microwerelden Hoofdstuk 33 in “The Cambridge Handbook of Multimedia Learning” met als titel: “Multimedia Learning in Games, Simulations and Microworlds”. Voor een lijst met door de auteur geciteerde bronnen zie [L.P. Rieber] Geciteerde bronnen pagina 42. Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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3.5.1 Definities gegeven door L.P. Rieber Educational game: Competitive rule-based activities involving one or more players with an expressed goal of performing or meeting a goal at a superior level (i.e., winning) either in relation to a previous performance level (one player game) or in relation to the performance levels of other players. Success in the activity requires use of subject matter in some way. Educational simulation: A computer program that models some phenomenon or activity and is designed to have participants learn about the phenomenon or activity through interaction with it. Participants usually have a defined role in the simulation. Microworld: An interactive, exploratory learning environment of a small subset of a domain that is immediately understandable by a user and also intrinsically motivating to the user. A microworld can be changed and modified by the user in order to explore the domain and to test hypotheses about the domain. ●

L. P. Rieber geeft een aantal voorbeelden om duidelijk te maken wat de verschillen zijn tussen 'simulaties' en 'microwerelden' volgens zijn definities ( “The Cambridge Handbook of Multimedia Learning” pagina 551..554). Ik geef hier het voorbeeld van één van de oudste programma's voor zogenaamde 'personal computers', namelijk “Flight Simulator”. Dit is wel degelijk een simulatie, want je kan er wel mee vliegen, maar je kan niet aan de vliegtuigen 'sleutelen', om bijvoorbeeld te proberen wat er gebeurt als je een ander soort schroef gebruikt...

●

Wat het 'spel' aspect betreft is het zo dat zowel bij een 'simulatie' als bij een 'micro wereld' een competitief aspect kan zitten, bijvoorbeeld dat de winnaar diegene is die het snelst van A naar B vliegt met hetzelfde type vliegtuig en bij dezelfde weersomstandigheden als zijn/haar concurrenten...

3.5.2 [L.P. Rieber] Educatieve simulaties en ermee leren Zie de definitie die door L.P. Rieber wordt gegeven [Educational simulation] L. P. Rieber geeft meer details over simulaties in “The Cambridge Handbook of Multimedia Learning” pagina 554..558.

3.5.2.1 [L.P. Rieber] De 2 onderzoeksdomeinen in verband met educatieve simulaties



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Two areas of research are discussed here: The first deals with how characteristics of the simulation designed to represent the underlying model are perceived and used by the students while interacting with the simulation... ...An example would be examining the use of graphics or text for key elements of the simulation's interface, such as how the simulation provides feedback... ...The second area of research examines student's scientific discovery learning with a simulation. This research concerns: the degree to which students are able to discover and understand the simulation's underlying model on their own, versus the degree to which they need varying levels of instructional support or guidance.

3.5.2.2 [L.P. Rieber] Het eerste onderzoeksdomein in verband met educatieve simulaties In this research, we examined the role of computer animation as graphical feedback during a simulation... ...The typical method we used in this research was to design three versions of a physics simulation... ...that varied in the way feedback was presented to users as they interacted with the simulation: [1] animated graphical feedback [2] textual feedback [3] or a combination of both graphical and textual feedback Samenvatting van de beschrijving door L.P. Rieber van het onderzoek naar het effect van elk van de 3 manieren om feedback te geven: Gebruik van een fysica simulatie: Similar to a video game, if the user wanted an object to move to the left, he or she would press a screen button that applied force to the object in the left-hand direction. In het geval van zuiver tekstuele feedback: Feedback is presented solely through the use of numeric readouts that [depict indicate] the current position [coordinates] of the object being manipulated and... ...the position [coordinates] of a target that participants are trying to hit with their object. In het geval van grafische feedback: ziet de student rechtstreeks de “target” en het gemanipuleerde object zelf alsook hun positie ten overstaan van elkaar. Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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Leren enkel door interactie met de simulatie ...we used a scientific discovery approach, that is, we deliberately did not include physics instruction to accompany the simulation. We wanted to be sure that all learning occurred solely through interaction with the simulation. Methode gebruikt voor het meten van de leerresultaten met iedere soort feedback: We evaluated learning in both explicit and implicit ways. Explicit learning was measured using a traditional multiple-choice test of physics understanding, whereas implicit learning was measured by the ability of students to complete a game-like activity similar to the simulation. Eerste resultaten: voorspellingen: As it turns out, this type of learning [solely through interaction with the simulation] is very difficult for students who are novices in physics. One prediction is that animated graphical feedback would be a better way to represent information and relationships for Newtonian mechanics – animated balls are close analogs to actual moving balls. However, such representations may not make the relationships explicit, that is, learning might remain implicit in the simulation activity unless the person makes a deliberate effort to “translate” the relationship into the verbal terms needed to answer the post-test questions. A person given the textual feedback who is successful at the simulation would have to work hard to make such a translation, thus leading to the prediction that textual feedback would lead to more explicit learning, especially when measured with verbally stated multiple-choice questions. Eerste resultaten: feiten: When the participants used the simulation in a discovery-oriented way, that is, without any accompanying instruction, results sometimes, but not always, favored the use of graphical feedback over textual feedback or graphical plus textual feedback on tests of explicit learning, such as traditional text-based questions (Rieber, 1969 ; Rieber et al., 1996 ). In contrast, graphical feedback led to superior performance on tests of implicit learning (computer-game-like tasks). In other words, graphical feedback was more beneficial than textual feedback for implicit, or neartransfer tasks. Yet, on explicit, or far-transfer tasks requiring students to translate graphical symbols into verbal symbols, the difference was not so compelling.



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Verdere resultaten: wat is er nodig om de studenten verder te helpen om de principes van de fysica voldoende expliciet te leren om verbaal gestelde problemen op te lossen? In follow-up qualitative studies where we interviewed participants as they used the simulation, we noticed that few people discovered the “secrets” of the laws of motion without some help or guidance by the interviewer... ...just pointing out some critical features to the participant[s] based on their experiences or simple questions that helped them to focus on key physical principles. The guidance did not even interrupt their interactions with the simulation, but instead seemed to give them insights for their subsequent attempts. What was needed was guiding information – an explanation – to be given at just the right time. Verdere resultaten: follow-up onderzoek over toegevoegde stukjes korte 'multimediale' uitleg: In a follow-up research study we included the use of short, embedded multimedia explanations (with both text and animation) of the physics principles and showed them to participants during the simulation [Rieber, L.P., Tzeng, S., & Tribble, K. (2004)]. Providing these short explanations made a huge difference. Participants given graphical feedback far outperformed those given textual feedback, but only when accompanied by the short explanations. Verdere resultaten: gemeten frustratie bij studenten na gebruik van de simulatie: We also measured the frustration levels of participants by asking students throughout the research (right after each simulation trial) to report their current level of frustration on a scale of 0 to 8... ...Participants given only textual feedback consistently reported much greater frustration than those given graphical feedback. This is not surprising because the mental effort in converting the numerical feedback into a spatial position on the screen is hard work.

3.5.2.3 [L.P. Rieber] Het tweede onderzoeksdomein in verband met educatieve simulaties The second area of simulation research reviewed here is that dealing with scientific discovery learning. The purpose of this research is to understand the process students go through to understand a simulation's underlying model in the absence of explicit instruction about the model. L.P. Rieber vermeldt de volgende bron voor één van de grondigste overzichten van dit soort onderzoek: [De Jong, T. & van Joolingen, W.R. (1998)]



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Their [Jong and Joolingen's] review demonstrates how difficult it is for students to use simulations in this way. [1] For example, students are often prone to confirmation bias, the tendency to design experiments that will lead them to confirm early formed hypotheses. Students find it difficult to discard hypotheses, even when faced with contradicting data. [2] Similarly, students find it difficult to construct hypotheses that can be easily tested with experiments. Students have difficulty in setting up an appropriate experiment to test even a well-stated hypotheses. Conclusies door de Jong en van Joolingen [De Jong, T. & van Joolingen, W.R. (1998)]: [1] Of the many conclusions offered by de Jong and van Joolingen, one is similar to that already discussed: information, guidance, or instructional support needs to be provided while students are using the simulation, as compared to extensive instructional treatments prior to using the simulation. [2] Another conclusion is that students benefit from simulations that progressively become more difficult and complex, doing so only as students gain expertise with earlier and simpler skills. [voorbeeld in “The Cambridge Handbook of Multimedia Learning” pagina 558 en Rieber, L.P., & Parmley, M.W. (1995)]

3.5.3 [L.P. Rieber] Micro werelden en ermee leren Zie de definitie die door L.P. Rieber wordt gegeven [Microworld] L. P. Rieber geeft meer details over micro werelden in “The Cambridge Handbook of Multimedia Learning” pagina 558..559. 3 doelen van micro werelden volgens L.P. Rieber: Although different conceptions of microworlds exist, three goals are common to all: First, they offer a way for more people, starting at a younger age, to understand and explore concepts and principles underlying complex systems. Second, microworlds focus primarily on qualitative understanding based on building and using concrete models. Third, there is a deliberate attempt to reduce the distinction between learning science and doing science. Indeed, the goal is to have students use technology in ways similar to those of a scientist. L.P. Rieber citeert de definitie gepubliceerd door A. diSessa [DiSessa, A. (2000)]: A microworld is a genre of computational document aimed at embedding important ideas in a form that students can readily explore. The best microworlds have an Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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easy-to-understand set of operations that students can use to engage tasks of value to them, and in doing so, they come to understand powerful underlying principles. You might come to understand ecology, for example, by building your own little creatures that compete with and are dependent on each other. L.P. Rieber beschrijft microwerelden enerzijds structureel, anderzijds functioneel: Structureel: (a) a collection of computational objects that model the mathematical or physical properties of the domain; (b) links to multiple representations of the underlying model; (c) opportunities or means to combine the computational objects in complex ways; and (d) inherent activities or challenges for the student to explore or solve in the domain. Functioneel: A functional analysis of a microworld focuses on the interaction between the student, the software, and the setting in which it is used. Students must be able to know how to use a microworld and want to use it. In other words, a microworld needs to match both the cognitive and affective states of the user. Students learn about a domain through exploration with the microworld. Resultaten van vroege research aangaande microwerelden: Research on microworlds in education has been contentious. Early research in the 1980s was focused on studying the effects of Logo [microwereld ontworpen door S. Papert om kinderen wiskunde te leren] on children's learning... ...Papert [Papert, S. (1987)] felt such research studies missed the point: “...Does wood produce good houses? If I built a house out of wood and it fell down, would this show that wood does not produce good houses? ... ...These betray themselves as technocentric questions by ignoring people and the elements only people can introduce: skill, design, aesthetics.” Papert took the view that Logo would be used by children to explore and learn mathematics as naturally as they learned language. However, several interesting research programs that began in the 1980s began to show the need for some imposed structure and designed activities in order for students to learn with microworlds [White, B.Y. (1984)].



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Research rond “design experiments” voor ontwikkelaars van microwerelden: A design experiment sets a specific pedagogical goal at the beginning and then seeks to determine the necessary organization, strategies, and technological support necessary to reach the goal. Such experiments involve an iterative and selfcorrecting process that resolves problems as they occur. The process is documented... ...design experiments offer the ability for a researcher to show the evolution of an innovation's design, implementation and use, rather than just focus on the results that come at the end of the design cycle.

3.5.4 [L.P. Rieber] Educatieve spellen en ermee leren Zie de definitie die door L.P. Rieber wordt gegeven: [Educational game] L. P. Rieber geeft meer details over spellen in “The Cambridge Handbook of Multimedia Learning” pagina 559..560. L.P. Rieber heeft het praktisch onmiddellijk over het resultaat van onderzoek: The research literature focusing on whether playing games leads to learning (i.e., gains in achievement) is mixed. When games are compared to traditional classroom instruction (the most common research method), few differences in learning are reported. Er wordt meer brood gezien in het door de studenten zelf laten ontwikkelen van spellen: Another approach to studying gaming is what can students learn from designing their own games... ...In these “children as designers” studies, elementary school students are typically given the task of designing an educational game for a younger audience... ...In one example, qualitative results showed how students used the design activity as an opportunity to engage in content-related discussions. Quantitative results also demonstrated increased learning of astronomy concepts by students... ...One research study focused on the following questions: (1) would children, other than those who designed the game, find these games motivating to play, and (2) based on the children's own play behavior, what features of these noncommercial games do children report as exemplary and noteworthy?... ...First, the children's ratings consistently matched their game playing behavior. That is, the games they chose to play most frequently and for the longest period were also the games they rated most favorably. The children's ratings were also stable and consistent over time. Three game characteristics favored by children included: (1) the quality of the game's storyline, Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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(2) competition, and (3) appropriate challenge.

3.5.5 [L.P. Rieber] Geciteerde bronnen De Jong, T. & van Joolingen, W.R. (1998) Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179-201. DiSessa, A., & Abelson, H. (1986) Boxer: A reconstructible computational medium. Communications of the ACM, 29(9), 859-868. DiSessa, A., Hoyles, C., Noss, R., & Edwards, L.D. (Eds.) (1995) Computers and exploratory learning. New York: Springer. DiSessa, A. (2000) Changing minds: Computers, learning, and literacy. Cambridge, MA: The MIT Press. Horwitz, P. (1999) Designing computer models that teach. In W. Feurzeig & N. Roberts (Eds.) Modeling and simulation in science and mathematics education (pp. 179-196). New York: Springer-Verlag. Olive, J. (1998) Opportunities to explore and integrate mathematics with “The Geometer's Sketchpad” in designing learning environments for developing understanding of geometry and space. In R. Lehrer & D. Chazan (Eds.) Designing learning environments for developing understanding of geometry and space (pp. 395418). Mahwah, NJ: Lawrence Erlbaum Associates. Papert, S. (1980b) Mindstorms: Children, computers, and powerful ideas. New York: BasicBooks. Papert, S. (1987) “Computer criticism vs. technocentric thinking.” Educational Researcher 1 6(1), 22-30. Resnick, M. (1994) Turtles, termites, and traffic jams. Cambridge, MA: MIT Press. Rieber, L.P. (1990) Using computer animated graphics in science instruction with children. Journal of Educational Psychology, 82, 135-140; Rieber, L.P. (1991) Animation, incidental learning, and continuing motivation. Journal of Educational Psychology, 83, 318-328; Rieber, L.P. (1996a) Animation as feedback in a computer-based simulation: Representation matters. Educational Technology Research and Development, 44(1), 522; Rieber, L.P. Boyce, M., & Assad, C. (1990) The effects of computer animation on adult learning and retrieval tasks. Journal of Computer-Based Instruction, 17(2), 46-52;



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Rieber, L.P., & Kini, A. (1995) Using computer simulations in inductive learning strategies with children in science. International Journal of Instructional Media, 22(2), 135-144; Rieber, L.P., Luke, N., & Smith, J. (1998) Project KID DESIGNER: Constructivism at work through play. Meridian: Middle School Computer Technology Journal, 1(1). Retrieved May 19, 2004 from http://www.ncsu.edu/meridian/archive_of_meridian/jan98/index.html ; Rieber, L.P., & Noah, D. (1997) Effect of gaming and graphical metaphors on reflective cognition within computer-based simulations. Paper presented at the annual meeting of the American Educational Research Association, Chicago, IL. ; Rieber, L.P., & Parmley, M.W. (1995) To teach or not to teach? Comparing the use of computer-based simulations in deductive versus inductive approaches to learning with adults in science. Journal of Educational Computing Research, 13(4), 359-374 ; Rieber, L.P., et al. (1996) The role of meaning in interpreting graphical and textual feedback during a computer-based simulation. Computers and Education, 27(1), 45-58 ; Rieber, L.P., Tzeng, S., & Tribble, K. (2004) Discovery learning, representation, and explanation within a computer-based simulation: Finding the right mix. Learning and Instruction, 14, 307-323. Roschelle, J. Kaput, J. & Stroup, W. (2000) SimCalc: Accelerating student engagement with the mathematics of change. In M.J. Jacobson & R.B. Kozma (Eds.) Learning the sciences of the 21st century: Research, design, and implementing advanced technology learning environments (pp. 47-75). Hillsdale, NJ: Lawrence Erlbaum Associates. White, B.Y. (1984) “Designing computer games to help physics students understand Newton's laws of motion.” Cognition and Instruction, 1(1), 69-108. White, B.Y. (1993) ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction, 10(1), 1-100. White, B.Y., & Horowitz, P. (1987) ThinkerTools: Enabling children to understand physical laws (No 6470). Cambridge, MA: Bolt, Beranek, and Newman.



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4

De ontstaansgeschiedenis van het Preventiespel

4.1 Mijn vorming en loopbaan Uitspraak van ‘Mijnheer Getteman’, onze onvergetelijke leraar Frans aan het Koninklijk Atheneum van Berchem (Antwerpen), na een bedroevend opstel over “L’amitié” [de vriendschap] door ons afgeleverd: Vous êtes tout juste bons à devenir ingénieurs… Mijn vertaling: Jullie zijn enkel goed genoeg om ingenieurs te worden… Op het moment dat ik dit schrijf (september 2011) ben ik een bijna 62 jaar oude ingenieur die in de zomer van 2003 door Alcatel Bell (België) met brugpensioen werd gestuurd in het kader van een herstructurering in de Alcatel groep die voorzag in 1000+ ontslagen en brugpensioenen in België. Ik groeide op in een typisch Belgische "mix" van Franse en Vlaamse cultuur invloeden. Mijn vader, een Waal maar perfect tweetalig (Frans en Nederlands) werkte voor de Antwerpse rechtbanken en mijn moeder, een Vlaamse, eindigde haar loopbaan in het onderwijs als directrice van een Katholieke meisjesschool in de naburige stad Mechelen. Mijn vader overleed in 1983 als gevolg van een longoedeem, mijn moeder in 1986 aan borstkanker. Ik heb een zuster, Mireille Rinchart (1956), die in het zuiden van Spanje woont met Diego, haar Spaanse levensgezel na het overlijden van haar echtgenoot in 2004, alsook met haar ondertussen volwassen kinderen, waarvan er 2 gehuwd zijn en zelf kinderen hebben. Mireille is daar in Spanje nog altijd werkzaam als kinesitherapeute en schoonheidsspecialiste. Ikzelf ben vrijgezel gebleven, maar heb toch heel wat ervaring met kinderen omdat ik altijd nauw betrokken ben geweest bij de opvoeding van de kinderen van Mireille en Jan toen ze nog in België woonden. Ik was hun 'speelkameraad' telkens ik op bezoek kwam en ben ontelbare keren babysitter geweest en onvermoeibaar verteller van verhalen vóór hun slapengaan. Na de lagere school en het middelbaar onderwijs aan het Koninklijk Atheneum van Berchem (Antwerpen), waar ik de richting Latijn-Wiskunde koos, studeerde ik in 1971 af aan het Stedelijk Instituut voor Hogere Technische Studiën van Antwerpen met de graad van industrieel ingenieur elektronica. Mijn carrière bij wat nu nog overblijft als “Alcatel-Lucent België” begon in 1973, toen het bedrijf nog “Bell Telephone Manufacturing Company”, heette, een onderdeel van het Amerikaanse ITT. Die carrière kan opgedeeld worden in 3 periodes van 10 jaar. De eerste 10 jaren werkte ik in de dienst opleiding, de eerste 2 jaren als cursus ontwikkelaar en 'leerfacilitator' voor de hardware van telefooncentrales van het Metaconta 10C type, de eerste computer-gestuurde centrales van Bell. Daarna werd ik als afgevaardigde van de dienst opleiding uitgestuurd naar de ontwikkeling afdeling NS Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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(“Nieuwe Systemen”) om de opleidingen voor te bereiden voor het digitale data schakelsysteem MCDS. De ontwikkeling van dat systeem werd echter stopgezet omdat een betere technologie voor data schakelen haar opmars begon, namelijk het pakketten schakelen dat ook gebruikt wordt in het Internet. Ik ben dan de opleidingen gaan voorbereiden voor het eerste digitale telefoon schakel systeem PCM-C, later 12T genoemd en tenslotte 'afgevoerd' omdat ITT besloten had alles te zetten op de ontwikkeling van System 12, waar voor het eerst vele kleinere, afzonderlijke microcomputers gebruikt werden in plaats van 2 grote centrale computers, wat de betrouwbaarheid ten goede kwam. De laatste 2 jaren van de eerste periode van 10 jaar ben ik door de opleiding afdeling gedetacheerd bij de ploeg van “Project TTA52”, een samenwerking tussen Bell en de Belgische ontwikkelingshulp om een studie te maken van de telefoon- en data communicatie noden in landelijke, dun bevolkte gebieden in Indonesië. Als onderdeel van die opdracht heb ik software ontwikkeld om te helpen bij het evalueren van netwerk structuur alternatieven na mezelf de programmeertaal Pascal aangeleerd te hebben. Omdat mijn interesse voor software ontwikkeling alsmaar groter werd en ik ervaring wat software ontwikkeling betreft had opgedaan tijdens mijn detachering bij de diverse ontwikkeling afdelingen en in het kader van “TTA52”, ben ik overgestapt naar de afdeling pakketten schakel systemen. Daar heb ik tijdens de 2de periode van 10 jaar in mijn carrière software ontwikkeld voor het DPS 1500 systeem. Na de overname van Bell door Alcatel en het 'opdoeken' van systeem DPS1500 ben ik teruggekeerd naar de afdeling opleiding, nu AUA (Alcatel University Antwerp) genaamd, waar ik mij hoofdzakelijk heb beziggehouden met het opleiden van software ontwikkelaars (basis data structuren, talen Pascal, C, C++ en Java) en het ontwikkelen van educatieve software, namelijk de “IP Analyzer” een simulatie van de IP-laag in een internet om de details van het IP protocol onder de knie te krijgen via het uitvoeren van experimenten op de simulatie en zo zelf te ervaren hoe alles werkt. Ik heb ook ervaring opgedaan met zogenaamde 'expert systemen' waarin kennis wordt opgeslagen onder de vorm van redeneer regels die 'voorwaarts' kunnen gebruikt worden (van oorzaak naar mogelijke gevolgen) of 'achterwaarts' (van gevolg naar mogelijke oorzaken). Dergelijke systemen worden onder andere in de geneeskunde gebruikt om te helpen bij de diagnose. Bij Alcatel worden ze gebruikt in de context van netwerk beheer en het rapporteren van netwerkproblemen.

4.2 Hoe mijn interesse voor de drugsproblematiek begon Eind 2004 werd ik geconfronteerd met een geval van drugsgebruik (cannabis) in mijn familie in Spanje. De reactie van de andere leden van het 'getroffen' gezin, waarin een 'vechtscheiding' bezig was, was er eerst één van bijna 'uitsluiting' van het bewuste familielid, ik denk hoofdzakelijk door gebrek aan kennis omtrent drugs en drugsgebruik en de problematiek er rond. Omdat ik zelf maar weinig afwist van drugs en de hele problematiek er rond, ondanks het feit dat ik 20 jaar was in 1969 (Hippies, Flower Power, Provo's, Communes, mei '68, ...), besloot ik om informatie te zoeken en een document te maken ten behoeve van Nederlandstalige 'residents' in Spanje. Dat is uiteindelijk het “DTA” document Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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geworden, zie: “Informatie over Drugs inclusief Tabak en Alcohol voor residents in Spanje (DTA)”. Als gevolg van het lezen van een document gemaakt in opdracht van de Spaanse overheid over de preventie in de schoot van het gezin van middelen misbruik door de kinderen, is hoofdstuk 7 van DTA ontstaan. Omdat dat Spaanse document zeer moeilijk om lezen is voor de doorsnee ouder en niet 'uitnodigt' om het volledig te lezen, besloot ik een aantal cases te maken om de ouders toe te laten na te denken over een aantal mogelijke reacties door hen op een case. Aldus groeide de gedachte bij mij om een spel te maken waarbij ouders moeten nadenken over mogelijke reacties op een aantal cases rond een aantal thema's in verband met preventie in de schoot van het gezin van drugs, tabak en alcohol misbruik. Wat ik toen zag als een 'must' was het maken van een computer spel, liefst een online spel, waarbij de ouders verantwoordelijk zijn voor de opvoeding van een 'virtueel kind', van geboorte tot volwassenheid. Een spel van een dergelijke complexiteit kan enkel praktisch bruikbaar zijn als het aan de hand van informatica middelen wordt geïmplementeerd.

4.3 Mijn 'mentor' en verdere ontwikkelingen Eens het DTA document af (“Informatie over Drugs inclusief Tabak en Alcohol voor residents in Spanje (DTA)”) was ik op zoek naar een deskundige om het te beoordelen en via Google ben ik terecht gekomen op de Website van wat toen (2006) nog de “Wetenschappelijke Vereniging van Vlaamse Huisartsen” heette, nu “Domus Medica”. Daar was Dr. Carl Steylaerts deskundige wat betreft drugs, en hij was enthousiast over mijn werk en over mijn plannen voor een preventiespel. Hijzelf is ook bezig met het ontwikkelen van educatieve spellen in het kader van het 'levenslang leren' van huisartsen. Hij is dan mijn 'mentor' geworden voor wat betreft educatieve spellen in 't algemeen en het Preventiespel in het bijzonder. Een eerste opmerking die hij maakte was de noodzaak voor een spel van 'papier en karton' omdat sommige kansarme gezinnen niet over een computer en breedband Internet toegang beschikken of omdat de ouders onvoldoende kennis hebben om met een computer te werken, terwijl juist in dergelijke gezinnen jongeren hopeloze situaties willen 'ontvluchten' via allerlei roes middelen waaronder ook levensgevaarlijke drugs. Zij gaan dan eventueel ook zelf dealen of zich prostitueren om hun gebruik te financieren en om in hun levensonderhoud te voorzien... Het is daarom dat ik de eerste versie van het Preventiespel ben beginnen ontwikkelen als een 'papier en karton' spel. Het gaat er niet om om geld te winnen maar om na te denken over reacties op 'uit het leven gegrepen' cases, zoals ik reeds voorzien had in hoofdstuk 7 van het DTA document (“Informatie over Drugs inclusief Tabak en Alcohol voor residents in Spanje (DTA)”). Het is rond die tijd dat ik op de boekenbeurs de 3 toen reeds verschenen boeken van Prof. Dr. Peter Adriaenssens gekocht heb, namelijk “Opvoeden is een groeiproces”, Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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“Van hieraf mag je gaan” en “Praten met je tiener”. Het is uit deze boeken dat ik de meeste bijkomende cases haalde (de eerste cases kwamen uit DTA) en er telkens 3 foutieve reacties op 'verzon' om tot een meerkeuze vraag te komen voor elke case.

4.4 Het “Slimmerkoken” intermezzo Ondertussen had een ex-collega van mij, Pascal Roobrouck, mij gevraagd of ik wilde meewerken aan zijn “Slimmerkoken” project, een Website rond beter en goedkoper koken die 'gehuisvest' zou worden via de diensten van een “server hosting provider” die het Linux besturingssysteem, de Apache Web server met PHP als programmeertaal voor de server kant en het MySQL relationeel database systeem aanbieden. Dit zijn allemaal zogenaamde “open source” software producten die tevens gratis te downloaden zijn, zodat de “server hosting providers” hun diensten tegen een lage huurprijs kunnen aanbieden (hun kosten zijn vooral in verband met de hardware, de energie en de koeling, de rechtstreekse Internet toegang met eigen vast IP adres alsook beheerskosten zoals het regelmatig nemen van “backups”). Een online versie van het Preventiespel zou ook dergelijke technologieën nodig hebben. Tijdens mijn loopbaan bij Bell heb ik wel ervaring opgedaan met het UNIX besturingssysteem (het grote voorbeeld voor de ontwikkelaars van Linux) en met het Oracle relationele database systeem en hun versie van SQL (taal om een relationele database te gebruiken), maar niet met Linux, noch met MySQL en de versie van SQL die MySQL gebruikt, en ook niet met PHP. Toen ik mijn akkoord had gegeven aan Pascal om met hem samen te werken was de situatie zo dat de goedkoopste “server hosting providers” enkel MySQL aanboden met de MyISAM “storage engine”. In de context van MySQL is een “storage engine” de software module die zich bezig houdt met de details betreffende het opslaan en beheren van de gegevens in de database. De MyISAM “storage engine” beschouwt elke SQL opdracht afzonderlijk en maakt de eventuele wijzigingen van gegevens onmiddellijk permanent. Zolang een transactie maar uit één SQL opdracht bestaat is er geen vuiltje aan de lucht. Bestaat een transactie echter uit meer dan één SQL opdracht, dan kan bij gebruik van MyISAM in geval van een fout tijdens het afhandelen van één van de SQL opdrachten de database achterblijven met een onvolledig uitgevoerde transactie, dus in een ongeldige toestand. Er bestaat een “storage engine” die wel transacties van meer dan één SQL opdracht ondersteunt, namelijk de InnoDB, maar die gebruikt met 'vlagen' meer werkgeheugen van de server en is trager. Bij gebruik van InnoDB kan er in geval van een fout automatisch een ROLLBACK opdracht gegeven worden die ervoor zorgt dat de database automatisch wordt teruggebracht naar de toestand van vóór het begin van de transactie, omdat InnoDB de wijzigingen slechts permanent maakt als de transactie afgesloten wordt met een COMMIT opdracht. Als een server uitgerust met InnoDB gedurende een lange tijd onvoldoende werkgeheugen heeft voor de lopende transacties van de verschillende klanten van de “server hosting provider” die eraan zijn toegewezen wordt het geheel erg traag omdat Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)


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constant gegevens heen en weer gaan naar/'terug van' het 'overloop' bestand, dat zowel bij Linux als bij UNIX, Microsoft Windows en Mac OS voorzien is om een tijdelijk gebrek aan werkgeheugen op te vangen. Een server uitgerust met MyISAM heeft kleinere 'pieken' in werkgeheugen gebruik en is dus gemakkelijker te dimensioneren. Gewapend met die kennis heb ik dan een systeem bedacht om bij gebruik van de MyISAM “storage engine” toch volwaardig transacties van meer dan één SQL opdracht te kunnen hebben via het automatisch samenstellen en onthouden van een 'tegengif' SQL opdracht voor elke wijzigende SQL opdracht in de transactie. Zo is het 'tegengif' voor een INSERT opdracht (die een rij toevoegt aan een database tabel) een DELETE opdracht (die die rij terug verwijdert). Wordt er een fout gedetecteerd tijdens een transactie dan zorgt een automatische “rollback” ervoor dat de 'tegengif' opdrachten in terugwaartse volgorde worden uitgevoerd om de database automatisch terug te brengen naar de toestand van vóór de onvolledige transactie. Met akkoord van Pascal ben ik dan begonnen met het ontwerpen en implementeren van mijn 'tegengif' systeem als een aantal 'klassen' ('modules') geschreven in de PHP programmeertaal. Helaas, zoals soms gebeurt in de snel evoluerende ICT wereld (Informatie en Communicatie Technologie wereld) is de situatie 'in 't veld' zodanig veranderd dat een ruime meerderheid van de goedkopere “server hosting providers” nu ook InnoDB ondersteunen, zodat mijn 'tegengif' systeem niet meer nodig is... Wat is nu het resultaat van dit alles? Dat ik de nodige ervaring heb opgedaan met MySQL en PHP en dus klaar ben voor de ontwikkeling van de online versies van het Preventiespel... Ik heb Pascal beloofd om alsnog mee te helpen als dat nodig zou zijn, want hij heeft nu een student die een eindejaarswerk rond dat “Slimmerkoken” gaat maken...

4.5 En nu, hoe gaat het verder met het Preventiespel? Het eerste wat ik ga afwerken is de 'papier en karton' kaartspel versie. Deze wordt kort beschreven verder in dit document. Zie De 'papier en karton' kaartspel versie.Voor de details van het design ervan wordt daar verwezen naar het specificatie en design document voor de 'papier en karton' versie. De tweede versie, de online versie van de kaartspel versie, heb ik voorzien om zo snel mogelijk een online versie te kunnen hebben. Met online bedoel ik dat de spelers gebruik maken van een Webbrowser (Internet Explorer, Google Chrome, Apple Safari, ...) om te communiceren met de server van het spel. Deze online versie maakt het spelen gemakkelijker en biedt een aantal extra mogelijkheden voor zowel de spelers als de ontwikkelaars. Zij wordt ook kort beschreven verder in dit document. Zie 6 De online kaartspel versie. Het design document voor deze online versie is nog niet beschikbaar ten tijde van het afwerken van de 0.4 versie van onderhavig document (“Het Preventiespel en zijn versies”).



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Verder beschouw ik nog kort 2 andere online versies die gebaseerd zijn op een simulatie van een kind ('virtueel kind') die als spel wordt gebruikt. Deze versies worden kort beschreven verder in onderhavig document. Zie 7 De 'virtueel kind' online versie en 8 De 'luchtkasteel'? 'virtueel kind' online versie.



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5

De 'papier en karton' kaartspel versie

5.1 Waarom Omdat veel kansarme gezinnen geen Internetabonnement hebben of de ouders niet weten hoe met een computer te werken, is deze 'papier en karton' kaartspel versie voorzien. Het is onder andere in dergelijke gezinnen dat kinderen de situatie thuis willen 'ontvluchten' door drugs te gebruiken en zelf te dealen of zich te prostitueren om hun gebruik te financieren en in hun levensonderhoud te voorzien. Een andere reden om deze 'papier en karton' versie te voorzien is vanwege de 'gezelligheid' om met een aantal bevriende koppels rond de tafel met een hapje en een drankje, al spelend tot een leerzame groepsdiscussie te komen over hoe de kinderen best op te voeden om het risico op drugsgebruik door hen zo klein mogelijk te houden.

5.2 Wat 5.2.1 Beperkingen Er zijn dusdanige praktische grenzen aan de complexiteit van een 'papier en karton' kaartspel dat een educatieve simulatie van een op te voeden kind, een 'virtueel' kind, praktisch gezien onmogelijk is. Met een educatieve simulatie van een kind kunnen de ouders bijna volledig 'opgaan' in hun taak als opvoeders ('situated' learning) en de gevolgen inzien van hun eventuele misstappen zonder dat er echt kwaad geschied is, want het is slechts een simulatie: ze kunnen het gesimuleerde kind laten terugkeren naar de toestand van vóór hun vergissing en via de gekregen feedback op de juiste manier verder gaan. Anderzijds kunnen we wel iets gebruiken dat zeer gemakkelijk kan geïmplementeerd worden met 'papier en karton', namelijk cases met voor elke case een aantal voorgestelde reacties erop die als vertrekpunten dienen voor een groepsdiscussie om de spelers te doen nadenken over de case. Het is daarop dat het 'papier en karton' kaartspel gebaseerd zal zijn.

5.2.2 Het spel moet in overeenstemming zijn met de beproefde theorieën over leren

5.2.2.1 In overeenstemming met de beproefde theorieën over leren in 't algemeen



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Zie 3.2.3 [T. Mayes en S. de Freitas] Learning theory and pedagogical design beginnend op pagina 14. Er zijn 3 perspectieven die belangrijk zijn voor het leren: Het associatief perspectief: The associationist approach models learning as the gradual building of patterns of associations and skill components. Learning occurs through the process of connecting the elementary mental or behavioral units, through sequences of activity followed by feedback. In het 'papier en karton' kaartspel wordt als 'activiteit' het oplossen van een probleem gebruikt, namelijk het zoeken van reacties op een 'geval' (case) via groepsdiscussies. Het cognitief perspectief: ...In school-level educational research the influence of Piaget has been very significant, in particular his assumption that conceptual development occurs through intellectual activity rather than by the absorption of information... Ook dit perspectief is terug te vinden in het 'papier en karton' kaartspel via de intellectuele activiteit tijdens het nadenken over een case in plaats van het direct horen uitspreken of lezen van een tekst die de juiste reactie beschrijft, zoals bij traditioneel 'ex cathedra' 'lesgeven'. Het situatief perspectief: ...A learner will always be subjected to influences from the social and cultural setting in which the learning occurs, which will also, at least partly, define the learning outcomes... Voor wat het 'papier en karton' kaartspel betreft wordt hiermee rekening gehouden door verschillende versies, niet alleen wat betreft talen, maar ook met cases aangepast aan de cultuur van de groep spelers.

5.2.2.2 In overeenstemming met de beproefde theorieën over leren door volwassenen en het faciliteren ervan Zie 3.4.3 [M.S. Knowles] Een theorie betreffende het leren door volwassenen: Andragogie en het andragogisch Model (Theory of learning) beginnend op pagina 24, en zie ook 3.4.4 [M.S. Knowles] Theories of teaching beginnend op pagina 27. The need to know Een volwassene wil weten waarom hij/zij iets moet leren. Dit kan gebeuren via een inleidende tekst in het overzicht document in de speldoos, of via de aanwezigheid van een facilitator tijdens een gezamenlijke speel sessie met een groep van spelers



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die verschillende teams vormen. Die facilitator kan dan ook direct antwoorden op vragen van de spelers, ook tijdens het spelen. The learner's self-concept Adults have a self-concept of being responsible for their own decisions, for their own lives. Once they have arrived at that self-concept they develop a deep psychological need to be seen by others and treated by others as being capable of self-direction. They resent and resist situations in which they feel others are imposing their wills on them... Waarom iets zus of zo is moet gemotiveerd worden. Tijdens de groepsdiscussies moeten de spelers respect tonen voor de mening van de andere speler(s), en de discussie mag ook niet ontaarden in een welles-nietes spelletje. The role of the learner's experiences ...for many kinds of learning, the richest resources for learning reside in the adult learners themselves. Hence, the emphasis in adult education is on experiential techniques – techniques that tap into the experience of the learners, such as group discussion, simulation exercises, problem-solving activities, case method, and laboratory methods instead of transmittal techniques. Also, greater emphasis is placed on peer-helping activities... In het 'papier en karton' kaartspel moet er over problemen nagedacht worden via de cases. Tevens worden groepsdiscussies tussen spelers voorzien in de spelregels. ...to adults, their experience is who they are. The implication of this fact for adult education is that in any situation in which the participants' experiences are ignored or devalued, adults will perceive this as rejecting not only their experience, but rejecting themselves as persons. In de documenten van het 'papier en karton' kaartspel of als een facilitator aanwezig is mag de ervaring van spelers niet 'weg gelachen' worden of zonder degelijk motief bekritiseerd. Readiness to learn Adults become ready to learn those things they need to know and be able to do in order to cope effectively with their real-life situations... Het heeft weinig zin om dit spel te laten spelen door een 10-jarige... maar des te meer door een koppel met een vurige kinderwens of waarvan de vrouw al zwanger is... Orientation to learning In contrast to children's and youth's subject-centered orientation to learning (at least in school), adults are life-centered (or task-centered or problem-centered) in their orientation to learning...



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Het 'papier en karton' kaartspel gebruikt cases, met problemen 'uit het leven gegrepen' waarover door de spelers moeten gediscuteerd worden. Motivation While adults are responsive to some external motivators (better jobs, promotions, higher salaries, and the like), the most potent motivators are internal pressures (the desire for increased job satisfaction, self-esteem, quality of life, and the like). In de basis documenten van het 'papier en karton' kaartspel, en als er een facilitator aanwezig is, moet 'alles uit de kast gehaald worden' om de spelers te motiveren om het spel volledig uit te spelen. Vereisten voor een facilitator om een lerende te helpen: Zie 3.4.4.1 [M.S. Knowles] Het helpen van volwassenen om te leren beginnend op pagina 27.

5.2.2.3 In overeenstemming met de beproefde theorieën over leren aan de hand van een spel Zie 3.5.4 [L.P. Rieber] Educatieve spellen en ermee leren beginnend op pagina 41 Uit onderzoek blijkt dat het gebruik van 'een' educatief spel praktisch even goede resultaten geeft als traditioneel 'lesgeven'. Over het gebruik van een educatief spel dat rekening houdt met de theorieën over leren en leren door volwassenen heb ik geen onderzoek gevonden. Maar ik denk dat het competitief aspect van een spel voor veel mensen aantrekkelijk is en ze zal aanzetten om er enthousiast aan deel te nemen. De spelregels moeten alleszins zodanig opgesteld worden dat het spel zo competitief mogelijk is.

5.2.3 Elementen van het 'papier en karton' kaartspel

5.2.3.1 De 'spelers' Wat we in dit spel een 'speler' noemen kan 1 persoon zijn of een 'koppel' dat samen speelt als een team. Volgens “Learning in Groups - A Handbook for face-to-face and online environments” is er een maximum van een 10-tal deelnemers voor een goede groepsdiscussie. Elke persoon, ook elke persoon die deel uitmaakt van een 'koppel', wordt beschouwd als een deelnemer aan de groepsdiscussies.



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Het maximum aantal personen voor een sessie van het spel is dus 10. Het minimum aantal 'spelers' is 3: voor elke te spelen case is er een moderator en 2 andere 'spelers'. De 'spelers' spelen om beurten. Als de 'speler' aan de beurt 1 persoon is, dan is hij/zij de moderator van de groepsdiscussie over de te spelen case. Als de 'speler' aan de beurt een 'koppel' is, dan kiezen zij welke van hen beiden de moderator zal zijn voor de te spelen case. Scores worden gegeven aan 'spelers'.

5.2.3.2 Cases en case kaarten 5.2.3.2.1 'Feit' cases en kaarten Dit zijn cases waarop maar één juiste reactie bestaat. Dit is het geval voor cases over de kennis omtrent drugs, tabak, en alcohol. De eigenschappen van drugs, tabak, en alcohol - waaronder het effect op een mens - zijn duidelijk bepaald door het resultaat van wetenschappelijk onderzoek. Het gaat hier om feiten. Op een 'Feit' case kaart staat o.a. de beschrijving van de case en een meerkeuzevraag, waarop de 'speler' aan de beurt een antwoord moet uitkiezen dat volgens die 'speler' het juiste is, eventueel na een groepsdiscussie. Die 'speler' kan dan achteraf in een apart document gaan kijken of die keuze de juiste is en waarom. Die 'speler' krijgt dan ofwel een aantal punten bij bij zijn totale score of niets.

5.2.3.2.2 'Enkel groepsdiscussie' cases en kaarten Aangezien er in de meeste gevallen meer dan één 'juiste' aanpak is voor een case, wordt er een groepsdiscussie georganiseerd, waarvoor de 'speler' aan de beurt de moderator levert. Na de beschrijving van de case staan er op de case kaart een aantal aangereikte vertrekpunten voor de groepsdiscussie, waaronder een vertrekpunt dat gebaseerd is op het werk van een gerespecteerd auteur, maar zonder vermelding dat dat het geval is. Voor Nederlandstaligen zijn dat de boeken van Prof. Dr. Peter Adriaenssens: “Opvoeden is een groeiproces”, “Van hieraf mag je gaan”, “Praten met je tiener”, en “Laat ze niet schieten”. Na de groepsdiscussie schrijft elke 'speler' zijn mening op een daarvoor voor één 'speler' bedoeld formulier dat die 'speler' na de spel sessie kan bijhouden. Een 'speler' kan ook een kopie vragen van de 'mening' documenten van de andere 'spelers'. Dr. Carl Steylaerts – mijn mentor – zegt dat we er van moeten uitgaan dat de groep [na de discussie] het antwoord op de vraag [hoe te reageren op de case] kent. Dat de groep dan een 'werkende' reactie op de case kent zal volgens mij in vele gevallen zo zijn, maar Auteur Guy Rinchart Uitgave 0.7 (0.x zijn nog te bespreken klad uitgaven)

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is niet altijd gegarandeerd. Daarom krijgen de 'spelers' referenties naar een aantal bronnen waarmee ze de mening van (een) gerespecteerd(e) auteur(s) kunnen opzoeken. Voor Nederlandstaligen zijn er nummers van bladzijden in de boeken van Prof. Dr. Peter Adriaenssens: “Opvoeden is een groeiproces”, “Van hieraf mag je gaan”, “Praten met je tiener”, en “Laat ze niet schieten”. Scores worden als volgt bepaald: ●

De moderator geeft aan elke andere 'speler' een score al naargelang zijn beoordeling van de bijdrage van die andere 'speler' tot de groepsdiscussie.

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Elke andere 'speler' geeft aan de 'speler' die de moderator levert een score al naargelang de beoordeling van die andere 'speler' van de prestatie van de moderator.

5.2.3.2.3 Kaartenbundels In sommige gevallen is het nodig dat een aantal cases altijd vlak na elkaar gespeeld worden. Dit is bijvoorbeeld het geval als het over een stappenplan gaat, zoals voor de zindelijkheidstraining van peuters. Om hieraan tegemoet te komen is er het concept van een kaartenbundel. Het gaat hier om een aantal case kaarten die fysisch aan elkaar gehecht zijn, zodat ze altijd als één geheel behandeld worden en in de gewenste volgorde gespeeld worden.

5.2.3.3 Fazen en thema's 5.2.3.3.1 Fazen in de ontwikkeling van een kind De volgende fazen in de ontwikkeling van een kind worden beschouwd: Fase 1: Zwangerschap en Baby jaren Fase 2: Kleuter jaren Fase 3: Vroege school periode Fase 4: Net vóór de adolescentie Fase 5: Vroege adolescentie Fase 6: Midden adolescentie Fase 7: Late adolescentie Fase 8: Eind fase van de adolescentie

(tot 4j) (4j..6j) (6j..10/11j) (10/11j..12/13j) (12/13j..14/15j) (14/15j..16/17j) (16/17j..20/21j) (20/21j..24/25j)

5.2.3.3.2 Thema's De volgende thema's worden beschouwd:



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Thema 1: Leer je kinderen feiten aangaande Drugs, Tabak en Alcohol en de gevolgen van het gebruik ervan.

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Thema 2: Evolutie van kinderen.

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Thema 3: Grenzen en normen en hun evolutie.

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Thema 4: Ken je kinderen zeer goed.

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Thema 5: Zorg voor het sociale bindweefsel in je gezin.

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Thema 6: Communicatie met tieners.

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Thema 7: Reacties van een ouder als die ontdekt dat één of meerdere van zijn/haar kinderen Drugs of Tabak of overmatig Alcohol gebruikt(gebruiken).

Deze thema's worden beschouwd omdat: ●

ze – wat thema's 2 tot en met 6 betreft – in de lijn liggen van de inhoud van de boeken van Prof. Dr. Peter Adriaenssens: “Opvoeden is een groeiproces”, “Van hieraf mag je gaan”, “Praten met je tiener”, en “Laat ze niet schieten”;

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het thema nummer voor thema's 2 tot en met 7 overeenstemt met de volgorde waarin die thema's moeten gespeeld worden als een fase wordt gespeeld.

5.2.3.4 Selectie van kaarten bij het begin van een spelsessie 5.2.3.4.1 De kaartenstapel Een spelsessie kan op 2 manieren gespeeld worden: ●

Voor een bepaalde fase

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Voor een bepaald thema

In beide gevallen moet er een kaartenstapel gemaakt worden met de case kaarten en kaartenbundels die moeten gespeeld worden. De 'spelers' zijn in wijzerzin aan de beurt en nemen dan telkens de bovenste kaart of kaartenbundel van de stapel en die wordt dan gespeeld. Na gespeeld te zijn wordt die kaart of kaartenbundel terug in de speldoos gestoken.

5.2.3.4.2 Selectie van kaarten/bundels voor het spelen van een bepaalde fase We noemen het nummer van de te spelen fase FN.



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Eerst worden de kaarten en kaartenbundels uit de speldoos genomen die bedoeld zijn voor de fase met nummer FN en thema 7 [Reacties van een ouder als die ontdekt dat één of meerdere van zijn/haar kinderen Drugs of Tabak of overmatig Alcohol gebruikt(gebruiken)]. Die kaarten worden 'geschud' en op tafel gelegd als de 'fundering' van de kaartenstapel. Als het fase nummer FN in de reeks van 4 tot en met 8 zit, worden ALLE kaarten van thema 6 [Communicatie met tieners] uit de speldoos genomen en in de omgekeerde volgorde waarin de kaarten van thema 6 moeten gespeeld worden (dus te beginnen met de laatste te spelen kaart) op de kaartenstapel gelegd. Dan worden de kaarten en kaartenbundels uit de speldoos genomen die bedoeld zijn voor de fase met nummer FN en thema 5 [Zorg voor het sociale bindweefsel in je gezin]. Die kaarten/bundels worden 'geschud' en op de kaartenstapel gelegd. Hetzelfde gebeurt voor thema 4 [Ken je kinderen zeer goed] tot en met thema 2 [Evolutie van kinderen]. Tenslotte worden ALLE kaarten voor thema 1 [Leer je kinderen feiten aangaande Drugs, Tabak en Alcohol en de gevolgen van het gebruik ervan] uit de speldoos genomen en eerst in de omgekeerde volgorde waarin ze moeten gespeeld worden op tafel gestapeld. Dan wordt de eerste kaart van die 'hulpstapel' genomen en bovenaan de kaartenstapel gelegd. De volgende kaart wordt dan van de 'hulpstapel' genomen en iets verder naar beneden in de kaartenstapel tussengevoegd. Dit gaat veder tot de laatste kaart van de 'hulpstapel' (de laatste te spelen kaart voor thema 1) is tussengevoegd. De kaartenstapel is nu gereed voor de spelsessie voor de fase met nummer FN

5.2.3.4.3 Selectie van kaarten/bundels voor het spelen van een thema We noemen het nummer van het te spelen thema TN. Als TN 1 of 6 is, dan worden de kaarten voor het thema met nummer TN uit de speldoos genomen en in de omgekeerde volgorde waarin ze moeten gespeeld worden op tafel gestapeld. De kaartenstapel is nu gereed voor de spelsessie voor thema 1 of 6. Als TN 2, 3, 4, 5 of 7 is, dan worden eerst de kaarten en kaartenbundels voor het thema met nummer TN uit de speldoos genomen die bedoeld zijn voor fase 8. Die worden 'geschud' en op tafel gelegd als de 'fundering' van de kaartenstapel. De kaarten en kaartenbundels voor het thema met nummer TN en fase 7 worden 'geschud' en op de kaartenstapel gelegd. Dit gaat zo door tot die voor het thema met nummer TN en fase 1. De kaartenstapel is nu gereed voor de spelsessie voor thema 2, 3, 4, 5 of 7.



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5.2.4 Details Het specificatie & design document voor de 'papier en karton' kaartspel versie bevat alle details. Zie “Specificatie van het Preventiespel als 'papier en karton' kaartspel”



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6

De online kaartspel versie

6.1 Wat wordt hier bedoeld met 'online' ? Met 'online' wordt hier bedoeld: ●

Dat Preventiespel sessies worden beheerd door een “server” computer die een vast Internet adres (IP adres) heeft en toegang tot het Internet en waarop de “Apache” “Web server” software 'draait' met ondersteuning voor de PHP “server” programmeertaal. Die “server” kan een MySQL database systeem contacteren dat eventueel op dezelfde “server” 'draait'.

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Dat 'spelers' gebruik maken van een toestel dat over een “Webbrowser” beschikt (de “client”: Microsoft Internet Explorer of Google Chrome of Apple Safari of...) die tekst opgestuurd door de “server” in de standaard HTML en Javascript talen kan 'verstaan' om een Web pagina weer te geven aan een 'speler' en uiteindelijk zijn/haar/hun “input” door te sturen naar de “server”.

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Dat het “Client/Server” HTTP protocol gebruikt wordt, het traditionele protocol voor communicatie tussen “clients” en “Web servers”, waarbij het initiatief uitgaat van een “Webbrowser” (dus van een “client”), normaal als gevolg van een verzoek van een 'speler'. Dit is het geval omdat een “client” niet noodzakelijk over een vast Internet-wijd adres (IP adres) beschikt, en omdat in een extreem geval zelfs een verschillend IP adres gebruikt wordt als adres van de afzender in elk verzoek dat naar een “server” gestuurd wordt door de “Internet service provider” van de 'speler'. Een “server” is dus niet in staat om zelf het initiatief te nemen om iets naar een “client” te sturen. Het is een “client” die een verzoek stuurt naar een “server” die (hopelijk!) een antwoord terugstuurt naar de “client” met normaal als IP adres van de bestemmeling het IP adres dat als afzender vermeld staat in het ontvangen verzoek van de “client”... Om ervoor te zorgen dat de pagina die in het browser venster aan de 'speler' wordt getoond 'up to date' blijft moet die browser regelmatig een verzoek sturen naar de “server” om de recentste inhoud toegestuurd te krijgen. Dat dit moet gebeuren kan aangeduid worden in de HTML code voor de bewuste Web pagina.

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Dat een “client” in ieder verzoek gericht aan de “server” de naam stuurt van een PHP bestand dat aanwezig is in de “server” alsook de waarde van de nodige parameters. De “Web server” zal de code in het PHP bestand uitvoeren, wat kan resulteren in het contacteren van een MySQL “database server” om gegevens uit een database te kunnen bekomen en gegevens in een database te kunnen wijzigen. Gegevens kunnen dan door de verdere uitvoering van de PHP code 'ingepakt' worden in HTML tekst met eventueel ook Javascript tekst (de 2 standaard talen om iets weer te geven in een “browser” venster en input van de gebruiker te



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verwerken/door te sturen). Het geheel wordt dan opgestuurd naar de “client” en door de “Webbrowser” gebruikt om de output van de “server” weer te geven in het “browser” venster van het toestel van de 'speler'.

6.2 Waarom: voordelen van de online kaartspel versie ●

Spelregels worden door de server automatisch toegepast.

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'Spelers' die her en der geografisch verspreid leven kunnen aan eenzelfde speelsessie deelnemen. Groepsdiscussies zijn mogelijk via chat of Skype video sessies. Zie http://www.skype.com/intl/nl/welcomeback/

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Als een speelsessie onderbroken wordt, wordt de toestand ervan onthouden in de database en kan er later vanuit die onthouden toestand verder gespeeld worden.

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De teksten voor een case, in een bepaalde taal in een bepaald land en voor een bepaalde cultuur, kunnen aan de hand van een Webbrowser (Internet Explorer, Google Chrome, Apple Safari, ...) ingevoerd worden in de database van het spel in een server via een gebruiksvriendelijke “interface” voorbehouden aan spel ontwerpers.

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Er kunnen in de database statistieken bijgehouden worden over de prestaties van de 'spelers'.

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Er zijn geen 8 documenten met case beschrijvingen en voorgestelde reacties op die cases meer, alles tezamen een 250-tal pagina's...

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Er moet door de 'spelers' normaal geen software geïnstalleerd worden. Er wordt enkel verondersteld dat een “Webbrowser” geïnstalleerd is in het toestel van de 'speler'.

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Door standaard talen en software te gebruiken (HTML en Javascript in de “client” en PHP en MySQL met InnoDB in de “server”) kan het spel op alle gangbare soorten computers met alle gangbare besturingssystemen gebruikt worden. Indien de organisatie die het Preventiespel ontplooit niet over (een) eigen server(s) beschikt, kan er gebruik gemaakt worden van de diensten van een “Server Provider” die MySQL met InnoDB ondersteunt. Tegenwoordig is de vereiste van MySQL met InnoDB te gebruiken geen probleem meer, ook niet bij de goedkoopste “Server Providers”.

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De 'spelers' spelen automatisch met de meest recente versie van het spel.



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6.3 Wat 6.3.1 Het spel moet in overeenstemming zijn met de beproefde theorieën over leren Wat dit betreft verandert er niets aan wat er betreffende de theorieën over leren besproken wordt in het hoofdstuk over de 'papier en karton' kaartspel versie. Zie 5.2.2 Het spel moet in overeenstemming zijn met de beproefde theorieën over leren

6.3.2 Registratie van spelers Spelers moeten zich registreren door een identiteit en bijhorend paswoord te kiezen. Zij zijn niet verplicht hun eigen naam als identiteit op te geven.

6.3.3 Elementen van het spel Dit zijn dezelfde als voor de 'papier en karton' versie. Zie 5.2.3 Elementen van het 'papier en karton' kaartspel

6.3.4 Extra voorzieningen Om de online versie van het kaartspel nog aantrekkelijker te maken komt er voor elke case een geanimeerde GIF formaat afbeelding die de case illustreert. Voor de communicatie met spelers die zich niet op dezelfde plaats bevinden wordt een tekst chat faciliteit voorzien. Indien er een audio/video communicatie gewenst is dient Skype gebruikt te worden, zie http://www.skype.com/intl/nl/welcomeback/

6.3.5 Details Meer details zullen te vinden zijn in het specificatie en design document voor het online kaartspel. Dit document is nog niet beschikbaar op het moment van publiceren van de eerste klad versie van onderhavig document (“Het Preventiespel en zijn versies”).



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7

De 'virtueel kind' online versie

7.1 Voorlopige korte omschrijving Om het spel nog meer tot de verbeelding te laten spreken voorzie ik ook een online versie die niet langer een kaartspel gebruikt maar elke speler een 'virtueel kind' laat kiezen met een bepaalde leeftijd en persoonlijkheid, kind dat de speler 'goed moet opvoeden' door de juiste reacties te selecteren uit de reacties die voorgesteld worden telkens een case moet opgelost worden door de speler aangaande zijn/haar 'virtuele kind' tijdens de verdere 'levensloop' ervan tot het einde van de adolescentie. Het gaat hier om een vorm van 'simulatie' van een kind. De cases zitten in een aantal boomstructuren, één per persoonlijkheid en per leeftijden bereik voor een 'virtueel kind' vanwaar kan vertrokken worden. De case in de wortel van een boomstructuur stelt 4 reacties voor op die case waaruit de speler er één moet kiezen. Die leidt dan naar een volgend knooppunt in de boomstructuur met daar weeral een case met 4 voorgestelde reacties. Afhankelijk van het pad dat door een speler gevolgd wordt doorheen de boomstructuur tijdens het 'opvoeden' van zijn/haar 'virtuele kind' moet eventueel de persoonlijkheid van het 'virtuele kind' door de simulatie aangepast worden. De spelers kunnen, zoals in de kaartspel versies, leerzame groepsdiscussies houden over het opvoeden van hun - in deze versie - 'virtuele kinderen'. Als het met die 'opvoeding' fout loopt, zal de speler de kans krijgen om 'terug te krabbelen' naar het knooppunt in de cases boomstructuur waar hij/zij een verkeerde reactie koos om van daaruit opnieuw verder te gaan met de juiste reactie (situaties die natuurlijk met een 'echt' kind niet altijd corrigeerbaar zijn, dit moet aan de speler duidelijk gemaakt worden). Hoe hoger de boomstructuren, des te 'gedetailleerder' de simulatie van het kind. Voor het ontwerpen van de cases in de boomstructuren in deze versie is gedegen kennis betreffende de opvoeding van kinderen vereist. Ikzelf zou mijn inbreng wat deze versie betreft beperken tot het ontwikkelen van de nodige software, terwijl de cases in de boomstructuren zouden kunnen ontworpen worden door studenten met de nodige specialisatie (Psychiatrie, Psychologie... ?), en aan de hand van een Webbrowser (Internet Explorer, Google Chrome, Apple Safari, ...) ingevoerd in de database van het spel in een server via een gebruiksvriendelijke “interface” voorbehouden aan spel ontwerpers.



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8

De 'luchtkasteel'? 'virtueel kind' online versie

8.1 Voorlopige korte omschrijving Tenslotte is er wat ik de 'luchtkasteel'? online versie noem. Daarin zal er zelfs geen sprake meer zijn van wel omlijnde cases zoals in de vorige online versies, maar zou het leven met een 'virtueel kind' waarvoor de 'speler' verantwoordelijk is verlopen via een simulatie die nog realistischer zou zijn (leidend tot 'situated' learning) dan die via cases zoals in 7 De 'virtueel kind' online versie. Een 'speler' zou ook moeten kunnen van gedachten wisselen met andere spelers over het opvoeden van elkaars kinderen. De verschillende 'virtuele kinderen' van de 'spelers' die deelnemen aan een sessie van het spel zouden moeten kunnen vrienden worden van de andere 'virtuele kinderen' en er eventueel 'relaties' mee hebben. Hamvraag is in hoeverre een systeem uit de informatica 'tak' 'Kunstmatige Intelligentie' (KI) in staat is om zo'n 'levensecht' 'virtueel kind' 'neer te zetten'...Uiteraard moet niet alles wat in een kind kan 'zitten' in dat 'virtuele kind' 'zitten' in de context van het Preventiespel, bijvoorbeeld niet alle kennis omtrent de vaderlandse geschiedenis... Over KI is het laatste woord nog lang niet gezegd, onder andere omdat er duidelijk moet gesteld worden wat men nu juist met 'intelligentie' bedoelt... Ik heb als appendix aan dit document een artikel toegevoegd van de Britse filosoof Jack Copeland dat de moeite waard is om het te lezen: Appendix A: Een inleiding in de Kunstmatige Intelligentie door filosoof Jack Copeland.



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9

Hoe het spel gebruiken in het kader van het grotere geheel van de preventie

Dit hoofdstuk moet nog geschreven worden.

9.1 Een aantal bronnen die ik al zeker zal gebruiken: 9.1.1 “Drug Abuse Prevention Through Family Interventions” Klik voor de URL van de download pagina voor een document dat een Amerikaans onderzoeksproject beschrijft uit de jaren '90 van de vorige eeuw betreffende preventie via de gezinnen van drugsgebruik door de kinderen.

9.1.2 “Laat ze niet schieten! - Geef de grens een plaats in het leven van jongeren” Klik voor gegevens over dit boek van Prof. Dr. Peter Adriaenssens. “We dreigen te veel van onze jongeren te verliezen in probleemgedrag. En dat is onze collectieve verantwoordelijkheid”... ...“Dit boek is een pleidooi om onze kinderen en jongeren beter vast te houden. Om hen duidelijke grenzen te geven, opnieuw te leren luisteren en solidair te zijn, niet zomaar omdat het moet maar omdat we hen leren ervoor te kiezen. 'Laat ze niet schieten' sluit af met een concreet actieplan, met dertig voorstellen om deze problematiek op verschillende niveaus – het gezin, het onderwijs, de samenleving – aan te pakken.”

9.1.3 “Harlem Children's Zone” Klik voor de URL van de start pagina van de website van deze organisatie van de Amerikaan Geoffrey Canada. Uittreksel uit een lezing door Dr. Carl Steylaerts van “Domus Medica”: “Na jaren van verloedering, criminaliteit, prostitutie en drugs zei ene Geoffrey Canada: dat moet anders kunnen. Hij zei dat omstreeks 1990, en hij sprak over allerlei goed bedoelde pogingen om de misdaad terug te dringen zonder veel effect. Harlem was een oorlogszone. Er zijn 2 observaties die aan de basis van zijn verandering aanpak liggen: ●

kinderen uit kansarme gezinnen hebben tegen de leeftijd dat ze naar het eerste leerjaar gaan 25 uur voorlezen gehad tegen 1700 uur voor kansrijken



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ze hebben 2,5 keer meer 'naar hun voeten gehad' dan dat ze complimenten kregen, de kansrijken hadden 6 keer meer complimenten gehad dan 'naar hun voeten'.

Dat maakte dat de kansrijken bij de start van het eerste leerjaar meer woorden en meer concepten kenden, en dus snel veel vertrouwen in eigen kunnen, wat hen rustiger en gelukkiger maakt dan hun kansarme leeftijdsgenoten. Na 20 jaar van projecten allerhande om de armoede te bestrijden zei hij: dat heeft hier niks geholpen. En in 1990 begon hij aan Harlem Children's Zone. Hij zei: 'we moeten bij de kinderen beginnen voor ze geboren zijn. Parenting Classes voor aankomende ouders. En daarna moeten we de eerste 3 jaar van hun leven een sterk netwerk rond hen bouwen. En vermits we maar een beperkt budget hebben, gaan we per 'block' werken.' 20 jaar later is het volgende geweten, o.a. door een Harvard studie: ●

voorheen waren 7% van de leerlingen op hun niveau, nu 97%

●

voorlopig zit men aan 97 blocks, en Obama wil het als voorbeeld voor Amerika.

●

Om dat te bereiken kost een kind tussen de 3500 en 5000 USD per jaar. 1/3 komt van de overheid, 2/3 van charity.”



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10 Referenties naar Boeken, Web documenten en Web sites 10.1 “Dictionary of Psychology” A.S. Reber, Penguin Reference, 2001. ISBN 978-0-14-051451-3

10.2 “Drug Abuse Prevention Through Family Interventions” Document in pdf formaat, URL van de download pagina voor het document:: http://www.nida.nih.gov/pdf/monographs/monograph177/download177.html

10.3 “Harlem Children's Zone” http://www.hcz.org/home

10.4 “Informatie over Drugs inclusief Tabak en Alcohol voor residents in Spanje (DTA)” G. Rinchart, document in pdf formaat via de Web pagina met als URL: http://users.skynet.be/bs130592/dta/

10.5 “Learning in Groups - A Handbook for face-to-face and online environments” D. Jaques and G. Salmon, Routledge, 4th edition, 2007, ISBN 978-0-415-36526-0

10.6 “Laat ze niet schieten! - Geef de grens een plaats in het leven van jongeren” P. Adriaenssens, Lannoo 2de druk, 2010, ISBN 978-90-209-9091-1

10.7 “Rethinking Pedagogy for a digital age” H. Beetham and R. Sharpe, editors, Routledge, 2007. ISBN 978-0-415-40874-5

10.8 “Specificatie van het Preventiespel als 'papier en karton' kaartspel” G.Rinchart, document in pdf formaat via de Web pagina met als URL: [voorlopig nog niet online]

10.9 “The Adult Learner” M.S. Knowles, Gulf Publishing Company, 1998. ISBN 0-88415-115-8



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10.10 “The Cambridge Handbook of Multimedia Learning” R.E. Mayer, editor, Cambridge University Press, 2005. ISBN 0-521-54751-2

10.11 “The Modern Practice of Adult Education” M.S. Knowles, Cambridge Adult Education, 1980. ISBN 0-8428-2213-5



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11 Appendix A: Een inleiding in de Kunstmatige Intelligentie door filosoof Jack Copeland http://www.alanturing.net/turing_archive/pages/Reference%20Articles/What%20is %20AI.html#I Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans. AI has had some success in limited, or simplified, domains. However, the five decades since the inception of AI have brought only very slow progress, and early optimism concerning the attainment of human-level intelligence has given way to an appreciation of the profound difficulty of the problem.

11.1 What is Intelligence? Quite simple human behaviour can be intelligent yet quite complex behaviour performed by insects is unintelligent. What is the difference? Consider the behaviour of the digger wasp, Sphex ichneumoneus. When the female wasp brings food to her burrow, she deposits it on the threshold, goes inside the burrow to check for intruders, and then if the coast is clear carries in the food. The unintelligent nature of the wasp's behaviour is revealed if the watching experimenter moves the food a few inches while the wasp is inside the burrow checking. On emerging, the wasp repeats the whole procedure: she carries the food to the threshold once again, goes in to look around, and emerges. She can be made to repeat this cycle of behaviour upwards of forty times in succession. Intelligence--conspicuously absent in the case of Sphex-is the ability to adapt one's behaviour to fit new circumstances. Mainstream thinking in psychology regards human intelligence not as a single ability or cognitive process but rather as an array of separate components. Research in AI has focussed chiefly on the following components of intelligence: learning, reasoning, problem-solving, perception, and language-understanding. Learning Learning is distinguished into a number of different forms. The simplest is learning by trial-anderror. For example, a simple program for solving mate-in-one chess problems might try out moves at random until one is found that achieves mate. The program remembers the successful move and next time the computer is given the same problem it is able to produce the answer immediately. The simple memorising of individual items--solutions to problems, words of vocabulary, etc.--is known as rote learning. Rote learning is relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalisation. Learning that involves generalisation leaves the learner able to perform better in situations not previously encountered. A program that learns past tenses of regular English verbs by rote will not be able to produce the past tense of e.g. "jump" until presented at least once with "jumped", whereas a program that is able to generalise from examples can learn the "add-ed" rule, and so form the past tense of "jump" in the absence of any previous encounter with this verb. Sophisticated modern techniques enable programs to generalise complex rules from data. Reasoning To reason is to draw inferences appropriate to the situation in hand. Inferences are classified as either deductive or inductive. An example of the former is "Fred is either in the museum or the café; he isn't in the café; so he's in the museum", and of the latter "Previous accidents just like this one have been caused by instrument failure; so probably this one was caused by instrument failure". The difference between the two is that in the deductive case, the truth of the premisses guarantees the truth of the conclusion, whereas in the inductive case, the truth of the premiss lends support to the conclusion that the accident was caused by instrument failure, but



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) nevertheless further investigation might reveal that, despite the truth of the premiss, the conclusion is in fact false. There has been considerable success in programming computers to draw inferences, especially deductive inferences. However, a program cannot be said to reason simply in virtue of being able to draw inferences. Reasoning involves drawing inferences that are relevant to the task or situation in hand. One of the hardest problems confronting AI is that of giving computers the ability to distinguish the relevant from the irrelevant. Problem-solving Problems have the general form: given such-and-such data, find x. A huge variety of types of problem is addressed in AI. Some examples are: finding winning moves in board games; identifying people from their photographs; and planning series of movements that enable a robot to carry out a given task. Problem-solving methods divide into special-purpose and general-purpose. A special-purpose method is tailor-made for a particular problem, and often exploits very specific features of the situation in which the problem is embedded. A general-purpose method is applicable to a wide range of different problems. One general-purpose technique used in AI is means-end analysis, which involves the step-by-step reduction of the difference between the current state and the goal state. The program selects actions from a list of means--which in the case of, say, a simple robot, might consist of pickup, putdown, moveforward, moveback, moveleft, and moveright-until the current state is transformed into the goal state. Perception In perception the environment is scanned by means of various sense-organs, real or artificial, and processes internal to the perceiver analyse the scene into objects and their features and relationships. Analysis is complicated by the fact that one and the same object may present many different appearances on different occasions, depending on the angle from which it is viewed, whether or not parts of it are projecting shadows, and so forth. At present, artificial perception is sufficiently well advanced to enable a self-controlled car-like device to drive at moderate speeds on the open road, and a mobile robot to roam through a suite of busy offices searching for and clearing away empty soda cans. One of the earliest systems to integrate perception and action was FREDDY, a stationary robot with a moving TV 'eye' and a pincer 'hand' (constructed at Edinburgh University during the period 1966-1973 under the direction of Donald Michie). FREDDY was able to recognise a variety of objects and could be instructed to assemble simple artefacts, such as a toy car, from a random heap of components. Language-understanding A language is a system of signs having meaning by convention. Traffic signs, for example, form a mini-language, it being a matter of convention that, for example, the hazard-ahead sign means hazard ahead. This meaning-by-convention that is distinctive of language is very different from what is called natural meaning, exemplified in statements like 'Those clouds mean rain' and 'The fall in pressure means the valve is malfunctioning'. An important characteristic of full-fledged human languages, such as English, which distinguishes them from, e.g. bird calls and systems of traffic signs, is their productivity. A productive language is one that is rich enough to enable an unlimited number of different sentences to be formulated within it. It is relatively easy to write computer programs that are able, in severely restricted contexts, to respond in English, seemingly fluently, to questions and statements, for example the Parry and Shrdlu programs described in the section Early AI Programs. However, neither Parry nor Shrdlu actually understands language. An appropriately programmed computer can use language without understanding it, in principle even to the point where the computer's linguistic behaviour is indistinguishable from that of a native human speaker of the language (see the section Is Strong AI Possible?). What, then, is involved in genuine understanding, if a computer that uses language indistinguishably from a native human speaker does not necessarily understand? There is no universally agreed answer to this difficult question. According to one theory, whether or not one understands depends not only upon one's behaviour but also upon one's



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) history: in order to be said to understand one must have learned the language and have been trained to take one's place in the linguistic community by means of interaction with other language-users.

11.2 Strong AI, Applied AI, and CS Research in AI divides into three categories: "strong" AI, applied AI, and cognitive simulation or CS. Strong AI aims to build machines whose overall intellectual ability is indistinguishable from that of a human being. Joseph Weizenbaum, of the MIT AI Laboratory, has described the ultimate goal of strong AI as being "nothing less than to build a machine on the model of man, a robot that is to have its childhood, to learn language as a child does, to gain its knowledge of the world by sensing the world through its own organs, and ultimately to contemplate the whole domain of human thought". The term "strong AI", now in wide use, was introduced for this category of AI research in 1980 by the philosopher John Searle, of the University of California at Berkeley. Some believe that work in strong AI will eventually lead to computers whose intelligence greatly exceeds that of human beings. Edward Fredkin, also of MIT AI Lab, has suggested that such machines "might keep us as pets". Strong AI has caught the attention of the media, but by no means all AI researchers view strong AI as worth pursuing. Excessive optimism in the 1950s and 1960s concerning strong AI has given way to an appreciation of the extreme difficulty of the problem, which is possibly the hardest that science has ever undertaken. To date, progress has been meagre. Some critics doubt whether research in the next few decades will produce even a system with the overall intellectual ability of an ant. Applied AI, also known as advanced information-processing, aims to produce commercially viable "smart" systems--such as, for example, a security system that is able to recognise the faces of people who are permitted to enter a particular building. Applied AI has already enjoyed considerable success. Various applied systems are described in this article. In cognitive simulation, computers are used to test theories about how the human mind works-for example, theories about how we recognise faces and other objects, or about how we solve abstract problems (such as the "missionaries and cannibals" problem described later). The theory that is to be tested is expressed in the form of a computer program and the program's performance at the task--e.g. face recognition--is compared to that of a human being. Computer simulations of networks of neurons have contributed both to psychology and to neurophysiology (some of this work is described in the section Connectionism). The program Parry, described below, was written in order to test a particular theory concerning the nature of paranoia. Researchers in cognitive psychology typically view CS as a powerful tool.



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11.3 Alan Turing and the Origins of AI The earliest substantial work in the field was done by the British logician and computer pioneer Alan Mathison Turing. In 1935, at Cambridge University, Turing conceived the modern computer. He described an abstract computing machine consisting of a limitless memory and a scanner that moves back and forth through the memory, symbol by symbol, reading what it finds and writing further symbols. The actions of the scanner are dictated by a program of instructions that is also stored in the memory in the form of symbols. This is Turing's "stored-program concept", and implicit in it is the possibility of the machine operating on, and so modifying or improving, its own program. Turing's computing machine of 1935 is now known simply as the universal Turing machine. All modern computers are in essence universal Turing machines. During the Second World War Turing was a leading cryptanalyst at the Government Code and Cypher School, Bletchley Park (where the Allies were able to decode a large proportion of the Wehrmacht's radio communications). Turing could not turn to the project of building a storedprogram elektronic computing machine until the cessation of hostilities in Europe in 1945. Nevertheless, during the wartime years he gave considerable thought to the issue of machine intelligence. Colleagues at Bletchley Park recall numerous off-duty discussions with him on the topic, and at one point Turing circulated a typewritten report (now lost) setting out some of his ideas. One of these colleagues, Donald Michie (who later founded the Department of Machine Intelligence and Perception at the University of Edinburgh), remembers Turing talking often about the possibility of computing machines (1) learning from experience and (2) solving problems by means of searching through the space of possible solutions, guided by rule-ofthumb principles. The modern term for the latter idea is "heuristic search", a heuristic being any rule-of-thumb principle that cuts down the amount of searching required in order to find the solution to a problem. Programming using heuristics is a major part of modern AI, as is the area now known as machine learning. At Bletchley Park Turing illustrated his ideas on machine intelligence by reference to chess. (Ever since, chess and other board games have been regarded as an important test-bed for ideas in AI, since these are a useful source of challenging and clearly defined problems against which proposed methods for problem-solving can be tested.) In principle, a chess-playing computer could play by searching exhaustively through all the available moves, but in practice this is impossible, since it would involve examining an astronomically large number of moves. Heuristics are necessary to guide and to narrow the search. Michie recalls Turing experimenting with two heuristics that later became common in AI, minimax and best-first. The minimax heuristic (described by the mathematician John von Neumann in 1928) involves assuming that one's opponent will move in such a way as to maximise their gains; one then makes one's own move in such a way as to minimise the losses caused by the opponent's expected move. The best-first heuristic involves ranking the moves available to one by means of a rule-of-thumb scoring system and examining the consequences of the highest-scoring move first. In London in 1947 Turing gave what was, so far as is known, the earliest public lecture to mention computer intelligence, saying "What we want is a machine that can learn from experience", adding that the "possibility of letting the machine alter its own instructions provides the mechanism for this". In 1948 he wrote (but did not publish) a report entitled "Intelligent Machinery". This was the first manifesto of AI and in it Turing brilliantly introduced many of the concepts that were later to become central, in some cases after reinvention by others. One of these was the concept of "training" a network of artificial neurons to perform specific tasks. In 1950 Turing introduced the test for computer intelligence that is now known simply as the Turing test. This involves three participants, the computer, a human interrogator, and a human "foil". The interrogator attempts to determine, by asking questions of the other two participants, which is the computer. All communication is via keyboard and screen. The interrogator may ask questions as penetrating and wide-ranging as he or she likes, and the computer is permitted to do everything possible to force a wrong identification. (So the computer might answer "No" in response to "Are you a computer?" and might follow a request to multiply one large number by another with a long pause and an incorrect answer.) The foil must help the interrogator to make



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) a correct identification. A number of different people play the roles of interrogator and foil, and if sufficiently many interrogators are unable to distinguish the computer from the human being then (according to proponents of the test) it is to be concluded that the computer is an intelligent, thinking entity. In 1991, the New York businessman Hugh Loebner started the annual Loebner Prize competition, offering a $100,000 prize for the first computer program to pass the Turing test (with $2,000 awarded each year for the best effort). However, no AI program has so far come close to passing an undiluted Turing test. In 1951 Turing gave a lecture on machine intelligence on British radio and in 1953 he published a classic early article on chess programming. Both during and after the war Turing experimented with machine routines for playing chess. (One was called the Turochamp.) In the absence of a computer to run his heuristic chess program, Turing simulated the operation of the program by hand, using paper and pencil. Play was poor! The first true AI programs had to await the arrival of stored-program elektronic digital computers.

11.4 Early AI Programs The first working AI programs were written in the UK by Christopher Strachey, Dietrich Prinz, and Anthony Oettinger. Strachey was at the time a teacher at Harrow School and an amateur programmer; he later became Director of the Programming Research Group at Oxford University. Prinz worked for the engineering firm of Ferranti Ltd, which built the Ferranti Mark I computer in collaboration with Manchester University. Oettinger worked at the Mathematical Laboratory at Cambridge University, home of the EDSAC computer. Strachey chose the board game of checkers (or draughts) as the domain for his experiment in machine intelligence. Strachey initially coded his checkers program in May 1951 for the pilot model of Turing's Automatic Computing Engine at the National Physical Laboratory. This version of the program did not run successfully; StracheyÕs efforts were defeated first by coding errors and subsequently by a hardware change that rendered his program obsolete. In addition, Strachey was dissatisfied with the method employed in the program for evaluating board positions. He wrote an improved version for the Ferranti Mark I at Manchester (with Turing's encouragement and utilising the latter's recently completed Programmers' Handbook for the Ferranti computer). By the summer of 1952 this program could, Strachey reported, "play a complete game of Draughts at a reasonable speed". Prinz's chess program, also written for the Ferranti Mark I, first ran in November 1951. It was for solving simple problems of the mate-in-two variety. The program would examine every possible move until a solution was found. On average several thousand moves had to be examined in the course of solving a problem, and the program was considerably slower than a human player. Turing started to program his Turochamp chess-player on the Ferranti Mark I but never completed the task. Unlike Prinz's program, the Turochamp could play a complete game and operated not by exhaustive search but under the guidance of rule-of-thumb principles devised by Turing. Machine learning Oettinger was considerably influenced by Turing's views on machine learning. His "Shopper" was the earliest program to incorporate learning (details of the program were published in 1952). The program ran on the EDSAC. Shopper's simulated world was a mall of eight shops. When sent out to purchase an item Shopper would if necessary search for it, visiting shops at random until the item was found. While searching, Shopper would memorise a few of the items stocked in each shop visited (just as a human shopper would). Next time Shopper was sent out for the same item, or for some other item that it had already located, it would go to the right shop straight away. As previously mentioned, this simple form of learning is called "rote learning" and is to be contrasted with learning involving "generalisation", which is exhibited by the program described next. Learning involving generalisation leaves the learner able to perform better in situations not previously encountered. (Strachey also investigated aspects of



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) machine learning, taking the game of NIM as his focus, and in 1951 he reported a simple rotelearning scheme in a letter to Turing.) The first AI program to run in the U.S. was also a checkers program, written in 1952 by Arthur Samuel of IBM for the IBM 701. Samuel took over the essentials of Strachey's program (which Strachey had publicised at a computing conference in Canada in 1952) and over a period of years considerably extended it. In 1955 he added features that enabled the program to learn from experience, and therefore improve its play. Samuel included mechanisms for both rote learning and generalisation. The program soon learned enough to outplay its creator. Successive enhancements that Samuel made to the learning apparatus eventually led to the program winning a game against a former Connecticut checkers champion in 1962 (who immediately turned the tables and beat the program in six games straight). To speed up learning, Samuel would set up two copies of the program, Alpha and Beta, on the same computer, and leave them to play game after game with each other. The program used heuristics to rank moves and board positions ("looking ahead" as many as ten turns of play). The learning procedure consisted in the computer making small numerical changes to Alpha's ranking procedure, leaving Beta's unchanged, and then comparing Alpha's and Beta's performance over a few games. If Alpha played worse than Beta, these changes to the ranking procedure were discarded, but if Alpha played better than Beta then Beta's ranking procedure was replaced with Alpha's. As in biological evolution, the fitter survived, and over many such cycles of mutation and selection the program's skill would increase. (However, the quality of learning displayed by even a simple living being far surpasses that of Samuel's and Oettinger's programs.) Evolutionary computing The work by Samuel just described was among the earliest in a field now called evolutionary computing and is an example of the use of a genetic algorithm or GA. The term "genetic algorithm" was introduced in about 1975 by John Holland and his research group at the University of Michigan, Ann Arbor. Holland's work is principally responsible for the current intense interest in GAs. GAs employ methods analogous to the processes of natural evolution in order to produce successive generations of software entities that are increasingly fit for their intended purpose. The concept in fact goes back to Turing's manifesto of 1948, where he employed the term "genetical search". The use of GAs is burgeoning in AI and elsewhere. In one recent application, a GA-based system and a witness to a crime cooperate to generate onscreen faces that become closer and closer to the recollected face of the criminal. Reasoning and problem-solving The ability to reason logically is an important aspect of intelligence and has always been a major focus of AI research. In his 1948 manifesto, Turing emphasised that once a computer can prove logical theorems it will be able to search intelligently for solutions to problems. (An example of a simple logical theorem is "given that either X is true or Y is true, and given that X is in fact false, it follows that Y is true".) Prinz used the Ferranti Mark I, the first commercially available computer, to solve logical problems, and in 1949 and 1951 Ferranti built two small experimental special-purpose computers for theorem-proving and other logical work. An important landmark in this area was a theorem-proving program written in 1955-1956 by Allen Newell and J. Clifford Shaw of the RAND Corporation at Santa Monica and Herbert Simon of the Carnegie Institute of Technology (now Carnegie-Mellon University). The program was designed to prove theorems from the famous logical work Principia Mathematica by Alfred North Whitehead and Bertrand Russell. In the case of one theorem, the proof devised by the program was more elegant than the proof given by Whitehead and Russell. The Logic Theorist, as the program became known, was the major exhibit at a conference organised in 1956 at Dartmouth College, New Hampshire, by John McCarthy, who subsequently became one of the most influential figures in AI. The title of the conference was "The Dartmouth Summer Research Project on Artificial Intelligence". This was the first use of



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) the term "Artificial Intelligence". Turing's original term "machine intelligence" has also persisted, especially in Britain. Newell, Simon and Shaw went on to construct the General Problem Solver, or GPS. The first version of GPS ran in 1957 and work continued on the project for about a decade. GPS could solve an impressive variety of puzzles, for example the "missionaries and cannibals" problem: How are a party of three missionaries and three cannibals to cross a river in a small boat that will take no more than two at a time, without the missionaries on either bank becoming outnumbered by cannibals? GPS would search for a solution in a trial-and-error fashion, under the guidance of heuristics supplied by the programmers. One criticism of GPS, and other programs that lack learning, is that the program's "intelligence" is entirely second-hand, coming from the programmer (mainly via the heuristics, in the case of GPS). Natural language communication Two of the best-known early programs are Eliza and Parry. Details of both were first published in 1966. These programs gave an eerie semblance of conversing intelligently. Parry, written by Stanford University psychiatrist Kenneth Colby, simulated a human paranoiac. Parry's responses are capitalised in the following extract from a "conversation" between Parry and a psychiatric interviewer.

Why are you in the hospital? I SHOULDN'T BE HERE. Who brought you here? THE POLICE. What trouble did you have with the police? COPS DON'T DO THEIR JOB. What do they do instead? COPS ARREST THE WRONG PEOPLE. They arrest the right ones sometimes. THE LAW SHOULD GET THOSE ITALIAN CROOKS. Are you Italian? MY BACKGROUND IS BRITISH BUT I WAS BORN IN THIS COUNTRY. Do you know any Italian crooks? I TRY TO AVOID THE UNDERWORLD. Psychiatrists who were asked to decide whether they were communicating with Parry or a human paranoiac were often unable to tell.

Eliza, written by Joseph Weizenbaum at MIT, simulated a human therapist. In the following extract, Eliza "speaks" second.

Men are all alike. IN WHAT WAY They're always bugging us about something or other. CAN YOU THINK OF A SPECIFIC EXAMPLE Well, my boyfriend made me come here. YOUR BOYFRIEND MADE YOU COME HERE He says I'm depressed much of the time. I AM SORRY TO HEAR THAT YOU ARE DEPRESSED It's true. I am unhappy. DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE UNHAPPY I need some help, that much seems certain. WHAT WOULD IT MEAN TO YOU IF YOU GOT SOME HELP



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) Neither Parry nor Eliza can reasonably be described as intelligent. Parry's contributions to the conversation are "canned"--constructed in advance by the programmer and stored away in the computer's memory. As the philosopher Ned Block says, systems like Parry are no more intelligent than is a juke box. Eliza, too, relies on canned sentences and simple programming tricks (such as editing and returning the remark that the human participant has just made).

11.5 AI Programming Languages In the course of their work on the Logic Theorist and GPS, Newell, Simon and Shaw developed their Information Processing Language, or IPL, a computer language tailored for AI programming. At the heart of IPL was a highly flexible data-structure they called a "list". A list is simply an ordered sequence of items of data. Some or all of the items in a list may themselves be lists. This leads to richly branching structures. In 1960 John McCarthy combined elements of IPL with elements of the lambda calculus--a powerful logical apparatus dating from 1936--to produce the language that he called LISP (from LISt Processor). In the U.S., LISP remains the principal language for AI work. (The lambda calculus itself was invented by Princeton logician Alonzo Church, while investigating the abstract Entscheidungsproblem, or decision problem, for predicate logic--the same problem that Turing was attacking when he invented the universal Turing machine.) The logic programming language PROLOG (from PROgrammation en LOGique) was conceived by Alain Colmerauer at the University of Marseilles, where the language was first implemented in 1973. PROLOG was further developed by logician Robert Kowalski, a member of the AI group at Edinburgh University. This language makes use of a powerful theorem-proving technique known as "resolution", invented in 1963 at the Atomic Energy Commission's Argonne National Laboratory in Illinois by the British logician Alan Robinson. PROLOG can determine whether or not a given statement follows logically from other given statements. For example, given the statements "All logicians are rational" and "Robinson is a logician", a PROLOG program responds in the affirmative to the query "Robinson is rational?". PROLOG is widely used for AI work, especially in Europe and Japan. Researchers at the Institute for New Generation Computer Technology in Tokyo have used PROLOG as the basis for sophisticated logic programming languages. These languages are in use on non-numerical parallel computers developed at the Institute. (The languages and the computers are known as "Fifth Generation" software and hardware.) Other recent work includes the development of languages for reasoning about time-dependent data such as "the account was paid yesterday". These languages are based on tense logic, a type of logic that permits statements to be located in the flow of time. (Tense logic was invented in 1953 by the philosopher Arthur Prior at the University of Canterbury, New Zealand.)

11.6 Micro-World AI The real world is full of distracting and obscuring detail: generally science progresses by focussing on artificially simple models of reality (in physics, frictionless planes and perfectly rigid bodies, for example). In 1970 Marvin Minsky and Seymour Papert, of the MIT AI Laboratory, proposed that AI research should likewise focus on developing programs capable of intelligent behaviour in artificially simple situations known as micro-worlds. Much research has focussed on the so-called blocks world, which consists of coloured blocks of various shapes and sizes arrayed on a flat surface. An early success of the micro-world approach was SHRDLU, written by Terry Winograd of MIT (details of the program were published in 1972). SHRDLU controlled a robot arm that operated above a flat surface strewn with play blocks (both the arm and the blocks were virtual). SHRDLU would respond to commands typed in natural English, such as "Will you please stack up both of the red blocks and either a green cube or a pyramid". The program would plan out a



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) sequence of actions and in the virtual world the robot arm would arrange the blocks appropriately. SHRDLU could correctly answer questions about its world of blocks, for example "Can a pyramid be supported by a pyramid?" (SHRDLU attempts to stack up two pyramids and fails) and "Is there anything which is bigger than every pyramid but is not as wide as the thing that supports it?" (to which SHRDLU answered "Yes, the blue block"). SHRDLU could also answer questions about its own actions: Had you touched any pyramid before you put one on the green block? YES, THE GREEN ONE. When did you pick it up? WHILE I WAS STACKING UP A RED CUBE, A LARGE GREEN CUBE, AND A LARGE RED BLOCK. Why?

TO GET RID OF IT. Why did you do that? TO CLEAN OFF THE RED CUBE. Why did you clear off that cube? TO PUT IT ON A LARGE GREEN CUBE. Why did you do that? TO STACK UP A RED CUBE, A LARGE GREEN CUBE, AND A LARGE RED BLOCK. Why did you do that? BECAUSE YOU ASKED ME TO. Although SHRDLU was initially hailed as a major breakthrough, Winograd soon announced that the program was in fact a dead end. The techniques pioneered in the program proved unsuitable for application in wider, more interesting worlds. Moreover, the appearance that Shrdlu gives of understanding the blocks micro-world, and English statements concerning it, is in fact an illusion. Shrdlu has no idea what a red block is. Another product of the micro-world approach was Shakey, a mobile robot developed at the Stanford Research Institute by Bertram Raphael, Nils Nilsson and their group, during the period 1968-1972. (Shakey can now be viewed at the Boston Computing Museum.) The robot occupied a specially built micro-world consisting of walls, doorways, and a few simply-shaped wooden blocks. Each wall had a carefully painted baseboard to enable the robot to "see" where the wall met the floor (a simplification of reality that is typical of the micro-world approach). Shakey had about a dozen basic abilities, such as TURN, PUSH and CLIMB-RAMP. These could be combined in various ways by the robot's planning programs. Shakey's primary sensor was a black-and-white television camera. Other sensors included a "bump bar", and odometry that enabled the robot to calculate its position by "dead reckoning". A demonstration video showed Shakey obeying an instruction to move a certain block from one room to another by locating a ramp, pushing the ramp to the platform on which the block happened to be located, trundling up the ramp, toppling the block onto the floor, descending the ramp, and manoeuvring the block to the required room, this sequence of actions having been devised entirely by the robot's planning program without human intervention. Critics emphasise the highly simplified nature of Shakey's environment and point out that, despite these simplifications, Shakey operated excruciatingly slowly--the sequence of actions in the demonstration video in fact took days to complete. The reasons for Shakey's inability to operate on the same time-scale as a human being are examined later in this article. FREDDY, a stationary robot with a TV "eye" mounted on a steerable platform, and a pincer "hand", was constructed at Edinburgh University under the direction of Donald Michie. FREDDY was able to recognise a small repertoire of objects, including a hammer, a cup and a ball, with about 95% accuracy; recognising a single object would take several minutes of computing time. The robot could be "taught" to assemble simple objects, such as a toy car, from a kit of parts. Envisaged applications included production-line assembly work and automatic parcel handling. FREDDY was conceived in 1966 but work was interrupted in 1973, owing to a change in the British Government's funding policy in the wake of a disparaging report on AI (and especially robotics) by the Cambridge mathematician Sir James Lighthill. Work on FREDDY resumed in 1982 with U.S. funding.



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) Roger Schank and his group at Yale applied a form of the micro-world approach to language processing. Their program SAM (1975) could answer questions about simple stories concerning stereotypical situations, such as dining in a restaurant and travelling on the subway. The program could infer information that was implicit in the story. For example, when asked "What did John order?", SAM replies "John ordered lasagne", even though the story states only that John went to a restaurant and ate lasagne. FRUMP, another program by Schank's group (1977), produced summaries in three languages of wire-service news reports. Impressive though SAM and FRUMP are, it is important to bear in mind that these programs are disembodied and have no real idea what lasagne and eating are. As critics point out, understanding a story requires more than an ability to produce strings of symbols in response to other strings of symbols. The greatest success of the micro-world approach is a type of programs known as an expert system.

11.7 Expert Systems An expert system is a computer program dedicated to solving problems and giving advice within a specialised area of knowledge. A good system can match the performance of a human specialist. The field of expert systems is the most advanced part of AI, and expert systems are in wide commercial use. Expert systems are examples of micro-world programs: their "worlds"-for example, a model of a ship's hold and the containers that are to be stowed in it--are selfcontained and relatively uncomplicated. Uses of expert systems include medical diagnosis, chemical analysis, credit authorisation, financial management, corporate planning, document routing in financial institutions, oil and mineral prospecting, genetic engineering, automobile design and manufacture, camera lens design, computer installation design, airline scheduling, cargo placement, and the provision of an automatic customer help service for home computer owners. The basic components of an expert system are a "knowledge base" or KB and an "inference engine". The information in the KB is obtained by interviewing people who are expert in the area in question. The interviewer, or "knowledge engineer", organises the information elicited from the experts into a collection of rules, typically of "if-then" structure. Rules of this type are called "production rules". The inference engine enables the expert system to draw deductions from the rules in the KB. For example, if the KB contains production rules "if x then y" and "if y then z", the inference engine is able to deduce "if x then z". The expert system might then query its user "is x true in the situation that we are considering?" (e.g. "does the patient have a rash?") and if the answer is affirmative, the system will proceed to infer z. In 1965 the AI researcher Edward Feigenbaum and the geneticist Joshua Lederberg, both of Stanford University, began work on Heuristic Dendral, the high-performance program that was the model for much of the ensuing work in the area of expert systems (the name subsequently became DENDRAL). The program's task was chemical analysis. The substance to be analysed might, for example, be a complicated compound of carbon, hydrogen and nitrogen. Starting from spectrographic data obtained from the substance, DENDRAL would hypothesise the substance's molecular structure. DENDRAL's performance rivalled that of human chemists expert at this task, and the program was used in industry and in universities. Work on MYCIN, an expert system for treating blood infections, began at Stanford in 1972. MYCIN would attempt to identify the organism responsible for an infection from information concerning the patient's symptoms and test results. The program would request further information if necessary, asking questions such as "has the patient recently suffered burns?". Sometimes MYCIN would suggest additional laboratory tests. When the program had arrived at a diagnosis it would recommend a course of medication. If requested, MYCIN would explain the reasoning leading to the diagnosis and recommendation. Examples of production rules from MYCIN's knowledge base are (1) If the site of the culture is blood, and the stain of the organism is gramneg, and the morphology of the organism is rod,



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) and the patient has been seriously burned, then there is evidence (.4) that the identity of the organism is pseudomonas. (The decimal number is a certainty factor, indicating the extent to which the evidence supports the conclusion.) (2) If the identity of the organism is pseudomonas then therapy should be selected from among the following drugs: Colistin (.98) Polymyxin (.96) Gentamicin (.96) Carbenicillin (.65) Sulfisoxazole (.64). (The decimal numbers represent the statistical probability of the drug arresting the growth of pseudomonas.) The program would make a final choice of drug from this list after quizzing the user concerning contra-indications such as allergies. Using around 500 such rules MYCIN achieved a high level of performance. The program operated at the same level of competence as human specialists in blood infections, and rather better than general practitioners. Janice Aikins' medical expert system Centaur (1983) was designed to determine the presence and severity of lung disease in a patient by interpreting measurements from pulmonary function tests. The following is actual output from the expert system concerning a patient at Pacific Medical Center in San Francisco. The findings about the diagnosis of obstructive airways disease are as follows: Elevated lung volumes indicate overinflation. The RV/TLC ratio is increased, suggesting a severe degree of air trapping. Low mid-expiratory flow is consistent with severe airway obstruction. Obstruction is indicated by curvature of the flow-volume loop which is of a severe degree. Conclusions: Smoking probably exacerbates the severity of the patient's airway obstruction. Discontinuation of smoking should help relieve the symptoms. Good response to bronchodilators is consistent with an asthmatic condition, and their continued use is indicated. Pulmonary function diagnosis: Severe obstructive airways disease, asthmatic type. Consultation finished. An important feature of expert systems is that they are able to work cooperatively with their human users, enabling a degree of human-computer symbiosis. AI researcher Douglas Lenat says of his expert system Eurisko, which became a champion player in the star-wars game Traveller, that the "final crediting of the win should be about 60/40% Lenat/Eurisko, though the significant point here is that neither Lenat nor Eurisko could have won alone". Eurisko and Lenat cooperatively designed a fleet of warships which exploited the rules of the Traveller game in unconventional ways, and which was markedly superior to the fleets designed by human participants in the game. Fuzzy logic Some expert systems use fuzzy logic. In standard, non-fuzzy, logic there are only two "truth values", true and false. This is a somewhat unnatural restriction, since we normally think of statements as being nearly true, partly false, truer than certain other statements, and so on. According to standard logic, however, there are no such in-between values--no "degrees of truth"--and any statement is either completely true or completely false. In 1920 and 1930 the Polish philosopher Jan Lukasiewicz introduced a form of logic that employs not just two values but many. Lotfi Zadeh, of the University of California at Berkeley, subsequently proposed that the many values of Lukasiewicz's logic be regarded as degrees of truth, and he coined the expression "fuzzy logic" for the result. (Zadeh published the first of many papers on the subject in 1965.) Fuzzy logic is particularly useful when it is necessary to deal with vague expressions, such as "bald", "heavy", "high", "low", "hot", "cold" and so on. Vague expressions are difficult to deal with in standard logic because statements involving them--"Fred is bald", say--may be neither completely true nor completely false. Non-baldness shades gradually into baldness, with no sharp dividing line at which the statement "Fred is bald" could change from being completely false to completely true. Often the rules that knowledge engineers elicit from human experts contain vague expressions, so it is useful if an expert system's inference engine employs fuzzy logic. An example of such a rule is: "If the pressure is high but not too high, then reduce the fuel flow a little". (Fuzzy logic is used elsewhere in AI, for example in robotics and in neuron-like computing. There are literally thousands of commercial applications of fuzzy logic, many developed in Japan, ranging from an automatic subway train controller to control systems for washing machines and cameras.) Limitations of expert systems



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) Expert systems have no "common sense". They have no understanding of what they are for, nor of what the limits of their applicability are, nor of how their recommendations fit into a larger context. If MYCIN were told that a patient who has received a gunshot wound is bleeding to death, the program would attempt to diagnose a bacterial cause for the patient's symptoms. Expert systems can make absurd errors, such as prescribing an obviously incorrect dosage of a drug for a patient whose weight and age are accidentally swapped by the clerk. One project aimed at improving the technology further is described in the next section. The knowledge base of an expert system is small and therefore manageable--a few thousand rules at most. Programmers are able to employ simple methods of searching and updating the KB which would not work if the KB were large. Furthermore, micro-world programming involves extensive use of what are called "domain-specific tricks"--dodges and shortcuts that work only because of the circumscribed nature of the program's "world". More general simplifications are also possible. One example concerns the representation of time. Some expert systems get by without acknowledging time at all. In their micro-worlds everything happens in an eternal present. If reference to time is unavoidable, the micro-world programmer includes only such aspects of temporal structure as are essential to the task--for example, that if a is before b and b is before c then a is before c. This rule enables the expert system to merge suitable pairs of before-statements and so extract their implication (e.g. that the patient's rash occurred before the application of penicillin). The system may have no other information at all concerning the relationship "before"--not even that it orders events in time rather than space. The problem of how to design a computer program that performs at human levels of competence in the full complexity of the real world remains open.

11.8 The CYC Project CYC (the name comes from "encyclopaedia") is the largest experiment yet in symbolic AI. The project began at the Microelektronics and Computer Technology Corporation in Texas in 1984 under the direction of Douglas Lenat, with an initial budget of U.S.$50 million, and is now Cycorp Inc. The goal is to build a KB containing a significant percentage of the common sense knowledge of a human being. Lenat hopes that the CYC project will culminate in a KB that can serve as the foundation for future generations of expert systems. His expectation is that when expert systems are equipped with common sense they will achieve an even higher level of performance and be less prone to errors of the sort just mentioned. By "common sense", AI researchers mean that large corpus of worldly knowledge that human beings use to get along in daily life. A moment's reflection reveals that even the simplest activities and transactions presuppose a mass of trivial-seeming knowledge: to get to a place one should (on the whole) move in its direction; one can pass by an object by moving first towards it and then away from it; one can pull with a string, but not push; pushing something usually affects its position; an object resting on a pushed object usually but not always moves with the pushed object; water flows downhill; city dwellers do not usually go outside undressed; causes generally precede their effects; time constantly passes and future events become past events ... and so on and so on. A computer that is to get along intelligently in the real world must somehow be given access to millions of such facts. Winograd, the creator of SHRDLU, has remarked "It has long been recognised that it is much easier to write a program to carry out abstruse formal operations than to capture the common sense of a dog". The CYC project involves "hand-coding" many millions of assertions. By the end of the first six years, over one million assertions had been entered manually into the KB. Lenat estimates that it will require some 2 person-centuries of work to increase this figure to the 100 million assertions that he believes are necessary before CYC can begin learning usefully from written material for itself. At any one time as many as 30 people may be logged into CYC, all simultaneously entering data. These knowledge-enterers (or "cyclists") go through newspaper and magazine articles, encyclopaedia entries, advertisements, and so forth, asking themselves what the writer assumed the reader would already know: living things get diseases, the products of a commercial process are more valuable than the inputs, and so on. Lenat describes CYC as "the complement of an encyclopaedia": the primary goal of the project is to encode the knowledge that any person or machine must have before they can begin to



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) understand an encyclopaedia. He has predicted that in the early years of the new millennium, CYC will become "a system with human-level breadth and depth of knowledge". CYC uses its common-sense knowledge to draw inferences that would defeat simpler systems. For example, CYC can infer "Garcia is wet" from the statement "Garcia is finishing a marathon run", employing its knowledge that running a marathon entails high exertion, that people sweat at high levels of exertion, and that when something sweats it is wet. Among the outstanding fundamental problems with CYC are (1) issues in search and problemsolving, for example how to automatically search the KB for information that is relevant to a given problem (these issues are aspects of the frame problem, described in the section Nouvelle AI) and (2) issues in knowledge representation, for example how basic concepts such as those of substance and causation are to be analyzed and represented within the KB. Lenat emphasises the importance of large-scale knowledge-entry and is devoting only some 20 percent of the project's effort to development of mechanisms for searching, updating, reasoning, learning, and analogizing. Critics argue that this strategy puts the cart before the horse.

11.9 Top-Down AI vs Bottom-Up AI Turing's manifesto of 1948 distinguished two different approaches to AI, which may be termed "top down" and "bottom up". The work described so far in this article belongs to the top-down approach. In top-down AI, cognition is treated as a high-level phenomenon that is independent of the low-level details of the implementing mechanism--a brain in the case of a human being, and one or another design of elektronic digital computer in the artificial case. Researchers in bottom-up AI, or connectionism, take an opposite approach and simulate networks of artificial neurons that are similar to the neurons in the human brain. They then investigate what aspects of cognition can be recreated in these artificial networks. The difference between the two approaches may be illustrated by considering the task of building a system to discriminate between W, say, and other letters. A bottom-up approach could involve presenting letters one by one to a neural network that is configured somewhat like a retina, and reinforcing neurons that happen to respond more vigorously to the presence of W than to the presence of aany other letter. A top-down approach could involve writing a computer program that checks inputs of letters against a description of W that is couched in terms of the angles and relative lengths of intersecting line segments. Simply put, the currency of the bottom-up approach is neural activity and of the top-down approach descriptions of relevant features of the task. The descriptions employed in the top-down approach are stored in the computer's memory as structures of symbols (e.g. lists). In the case of a chess or checkers program, for example, the descriptions involved are of board positions, moves, and so forth. The reliance of top-down AI on symbolically encoded descriptions has earned it the name "symbolic AI". In the 1970s Newell and Simon--vigorous advocates of symbolic AI--summed up the approach in what they called the Physical Symbol System Hypothesis, which says that the processing of structures of symbols by a digital computer is sufficient to produce artificial intelligence, and that, moreover, the processing of structures of symbols by the human brain is the basis of human intelligence. While it remains an open question whether the Physical Symbol System Hypothesis is true or false, recent successes in bottom-up AI have resulted in symbolic AI being to some extent eclipsed by the neural approach, and the Physical Symbol System Hypothesis has fallen out of fashion.

11.10 Connectionism Connectionism, or neuron-like computing, developed out of attempts to understand how the brain works at the neural level, and in particular how we learn and remember.



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A natural neural network. The Golgi method of staining brain tissue renders the neurons and their interconnecting fibres visible in silhouette.

In one famous connectionist experiment (conducted at the University of California at San Diego and published in 1986), David Rumelhart and James McClelland trained a network of 920 artificial neurons to form the past tenses of English verbs. The network consisted of two layers of 460 neurons:

Each of the 460 neurons in the input layer is connected to each of the 460 neurons in the output layer Root forms of verbs--such as "come", "look", and "sleep"--were presented (in an encoded form) to one layer of neurons, the input layer. A supervisory computer program observed the difference between the actual response at the layer of output neurons and the desired response--"came", say--and then mechanically adjusted the connections throughout the network in such a way as to give the network a slight push in the direction of the correct response (this procedure is explained in more detail in what follows). About 400 different verbs were presented one by one to the network and the connections were adjusted after each presentation. This whole procedure was repeated about 200 times using the same verbs. By this stage the network had learned its task satisfactorily and would correctly form the past tense of unfamiliar verbs as well as of the original verbs. For example, when presented for the first time with "guard" the network responded "guarded", with "weep" "wept", with "cling" "clung", and with "drip" "dripped" (notice the double "p"). This is a striking example of learning involving generalisation. (Sometimes, though, the peculiarities of English were too


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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) much for the network and it formed "squawked" from "squat", "shipped" from "shape", and "membled" from "mail".) The simple neural network shown below illustrates the central ideas of connectionism.

A patternclassifier Four of the network's five neurons are for input and the fifth--to which each of the others is connected--is for output. Each of the neurons is either firing (1) or not firing (0). This network can learn to which of two groups, A and B, various simple patterns belong. An external agent is able to "clamp" the four input neurons into a desired pattern, for example 1100 (i.e. the two neurons to the left are firing and the other two are quiescent). Each such pattern has been preassigned to one of two groups, A and B. When a pattern is presented as input, the trained network will correctly classify it as belonging to group A or group B, producing 1 as output if the pattern belongs to A, and 0 if it belongs to B (i.e. the output neuron fires in the former case, does not fire in the latter). Each connection leading to N, the output neuron, has a "weight". What is called the "total weighted input" into N is calculated by adding up the weights of all the connections leading to N from neurons that are firing. For example, suppose that only two of the input neurons, X and Y, are firing. Since the weight of the connection from X to N is 1.5 and the weight of the connection from Y to N is 2, it follows that the total weighted input to N is 3.5. N has a "firing threshold" of 4. That is to say, if N's total weighted input exceeds or equals N's threshold, then N fires; and if the total weighted input is less than the threshold, then N does not fire. So, for example, N does not fire if the only input neurons to fire are X and Y, but N does fire if X, Y and Z all fire. Training the network involves two steps. First, the external agent inputs a pattern and observes the behaviour of N. Second, the agent adjusts the connection-weights in accordance with the rules: (1) If the actual output is 0 and the desired output is 1, increase by a small fixed amount the weight of each connection leading to N from neurons that are firing (thus making it more likely that N will fire next time the network is given the same pattern) (2) If the actual output is 1 and the desired output is 0, decrease by that same small amount the weight of each connection leading to the output neuron from neurons that are firing (thus making it less likely that the output neuron will fire the next time the network is given that pattern as input).



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) The external agent--actually a computer program--goes through this two-step procedure with each of the patterns in the sample that the network is being trained to classify. The agent then repeats the whole process a considerable number of times. During these many repetitions, a pattern of connection weights is forged that enables the network to respond correctly to each of the patterns. The striking thing is that the learning process is entirely mechanistic and requires no human intervention or adjustment. The connection weights are increased or decreased mechanically by a constant amount and the procedure remains the same no matter what task the network is learning. Another name for connectionism is "parallel distributed processing" or PDP. This terminology emphasises two important features of neuron-like computing. (1) A large number of relatively simple processors--the neurons--operate in parallel. (2) Neural networks store information in a distributed or holistic fashion, with each individual connection participating in the storage of many different items of information. The know-how that enables the past-tense network to form "wept" from "weep", for example, is not stored in one specific location in the network but is spread through the entire pattern of connection weights that was forged during training. The human brain also appears to store information in a distributed fashion, and connectionist research is contributing to attempts to understand how the brain does so. Recent work with neural networks includes: (1) The recognising of faces and other objects from visual data. A neural network designed by John Hummel and Irving Biederman at the University of Minnesota can identify about ten objects from simple line drawings. The network is able to recognise the objects--which include a mug and a frying pan--even when they are drawn from various different angles. Networks investigated by Tomaso Poggio of MIT are able to recognise (a) bent-wire shapes drawn from different angles (b) faces photographed from different angles and showing different expressions (c) objects from cartoon drawings with grey-scale shading indicating depth and orientation. (An early commercially available neuron-like face recognition system was WISARD, designed at the beginning of the 1980s by Igor Aleksander of Imperial College London. WISARD was used for security applications.) (2) Language processing. Neural networks are able to convert handwriting and typewritten material to standardised text. The U.S. Internal Revenue Service has commissioned a neuronlike system that will automatically read tax returns and correspondence. Neural networks also convert speech to printed text and printed text to speech. (3) Neural networks are being used increasingly for loan risk assessment, real estate valuation, bankruptcy prediction, share price prediction, and other business applications. (4) Medical applications include detecting lung nodules and heart arrhythmia, and predicting patients' reactions to drugs. (5) Telecommunications applications of neural networks include control of telephone switching networks and echo cancellation in modems and on satellite links.

11.10.1 History of Connectionism In 1933 the psychologist Edward Thorndike suggested that human learning consists in the strengthening of some (then unknown) property of neurons, and in 1949 psychologist Donald Hebb suggested that it is specifically a strengthening of the connections between neurons in the brain that accounts for learning. In 1943, the neurophysiologist Warren McCulloch of the University of Illinois and the mathematician Walter Pitts of the University of Chicago published an influential theory according to which each neuron in the brain is a simple digital processor and the brain as a whole is a form of computing machine. As McCulloch put it subsequently, "What we thought we were doing (and I think we succeeded fairly well) was treating the brain as a Turing machine". McCulloch and Pitts gave little discussion of learning and apparently did not envisage fabricating networks of artificial neuron-like elements. This step was first taken, in concept, in



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) 1947-48, when Turing theorized that a network of initially randomly connected artificial neurons--a Turing Net--could be "trained" (his word) to perform a given task by means of a process that renders certain neural pathways effective and others ineffective. Turing foresaw the procedure--now in common use by connectionists--of simulating the neurons and their interconnections within an ordinary digital computer (just as engineers create virtual models of aircraft wings and skyscrapers). However, Turing's own research on neural networks was carried out shortly before the first stored-program electronic computers became available. It was not until 1954 (the year of Turing's death) that Belmont Farley and Wesley Clark, working at MIT, succeeded in running the first computer simulations of small neural networks. Farley and Clark were able to train networks containing at most 128 neurons to recognise simple patterns (using essentially the training procedure described above). In addition, they discovered that the random destruction of up to 10% of the neurons in a trained network does not affect the network's performance at its task--a feature that is reminiscent of the brain's ability to tolerate limited damage inflicted by surgery, an accident, or disease. During the 1950s neuron-like computing was studied on both sides of the Atlantic. Important work was done in England by W.K. Taylor at University College, London, J.T. Allanson at Birmingham University, R.L. Beurle and A.M. Uttley at the Radar Research Establishment, Malvern; and in the U.S. by Frank Rosenblatt, at the Cornell Aeronautical Laboratory. In 1957 Rosenblatt began investigating artificial neural networks that he called "perceptrons". Although perceptrons differed only in matters of detail from types of neural network investigated previously by Farley and Clark in the U.S. and byTaylor, Uttley, Beurle and Allanson in Britain, Rosenblatt made major contributions to the field, through his experimental investigations of the properties of perceptrons (using computer simulations), and through his detailed mathematical analyses. Rosenblatt was a charismatic communicator and soon in the U.S. there were many research groups studying perceptrons. Rosenblatt and his followers called their approach connectionist to emphasise the importance in learning of the creation and modification of connections between neurons and modern researchers in neuron-like computing have adopted this term. Rosenblatt distinguished between simple perceptrons with two layers of neurons--the networks described earlier for forming past tenses and classifying patterns both fall into this category-and multi-layer perceptrons with three or more layers.



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A three-layer perceptron. Between the input layer (bottom) and the output layer (top) lies a so-called 'hidden layer' of neurons.

One of Rosenblatt's important contributions was to generalise the type of training procedure that Farley and Clark had used, which applied only to two-layer networks, so that the procedure can be applied to multi-layer networks. Rosenblatt used the phrase "back-propagating error correction" to describe his method. The method, and the term "back-propagation", are now in everyday use in neuron-like computing (with improvements and extensions due to Bernard Widrow and M.E. Hoff, Paul Werbos, David Rumelhart, Geoffrey Hinton, Ronald Williams, and others). During the 1950s and 1960s, the top-down and bottom-up approaches to AI both flourished, until in 1969 Marvin Minsky and Seymour Papert of MIT, who were both committed to symbolic AI, published a critique of Rosenblatt's work. They proved mathematically that there are a variety of tasks that simple two-layer perceptrons cannot accomplish. Some examples they gave are: (1) No two-layer perceptron can correctly indicate at its output neuron (or neurons) whether there are an even or an odd number of neurons firing in its input layer. (2) No two-layer perceptron can produce at its output layer the exclusive disjunction of two binary inputs X and Y (the so-called "XOR problem").



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The exclusive disjunction of two binary inputs X and Y is defined by this table. It is important to realise that the mathematical results obtained by Minsky and Papert about twolayer perceptrons, while interesting and technically sophisticated, showed nothing about the abilities of perceptrons in general, since multi-layer perceptrons are able to carry out tasks that no two-layer perceptron can accomplish. Indeed, the "XOR problem" illustrates this fact: a simple three-layer perceptron can form the exclusive disjunction of X and Y (as Minsky and Papert knew). Nevertheless, Minsky and Papert conjectured--without any real evidence--that the multi-layer approach is "sterile" (their word). Somehow their analysis of the limitations of two-layer perceptrons convinced the AI community--and the bodies that fund it--of the fruitlessness of pursuing work with neural networks, and the majority of researchers turned away from the approach (although a small number remained loyal). This hiatus in research into neuron-like computing persisted for well over a decade before a renaissance occurred. Causes of the renaissance included (1) a widespread perception that symbolic AI was stagnating (2) the possibility of simulating larger and more complex neural networks, owing to the improvements that had occurred in the speed and memory of digital computers, and (3) results published in the early and mid 1980s by McClelland, Rumelhart and their research group (for example, the past-tenses experiment) which were widely viewed as a powerful demonstration of the potential of neural networks. There followed an explosion of interest in neuron-like computing, and symbolic AI moved into the back seat.

11.11 Nouvelle AI The approach to AI now known as "nouvelle AI" was pioneered at the MIT AI Laboratory by the Australian Rodney Brooks, during the latter half of the 1980s. Nouvelle AI distances itself from traditional characterisations of AI, which emphasize human-level performance. One aim of nouvelle AI is the relatively modest one of producing systems that display approximately the same level of intelligence as insects. Practitioners of nouvelle AI reject micro-world AI, emphasising that true intelligence involves the ability to function in a real-world environment. A central idea of nouvelle AI is that the basic building blocks of intelligence are very simple behaviours, such as avoiding an object, and moving forward. More complex behaviours "emerge" from the interaction of these simple behaviours. For example, a micro-robot whose simple behaviours are (1) collision-avoidance and (2) motion toward a moving object will appear to chase the moving object while hanging back from it a little. Brooks focussed in his initial work on building robots that behave somewhat like simplified insects (and in doing so he deliberately turned away from traditional characterisations of AI such as the one given at the beginning of this article). Examples of his insect-like mobile robots are Allen (after Allen Newell) and Herbert (after Herbert Simon). Allen has a ring of twelve ultrasonic sonars as its primary sensors and three independent behaviour-producing modules. The lowest-level module makes the robot avoid both stationary and moving objects. With only this module activated, Allen sits in the middle of a room until approached and then scurries away, avoiding obstacles as it goes. The second module makes the robot wander about at random when not avoiding objects, and the third pushes the robot to look for distant places with its sensors and to move towards them. (The second and third modules are in tension--just as



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) our overall behaviour may sometimes be the product of conflicting drives, such as the drive to seek safety and the drive to avoid boredom.) Herbert has thirty infrared sensors for avoiding local obstacles, a laser system that collects three-dimensional depth data over a distance of about twelve feet in front of the robot, and a hand equipped with a number of simple sensors. Herbert's real-world environment consists of the busy offices and work-spaces of the AI lab. The robot searches on desks and tables in the lab for empty soda cans, which it picks up and carries away. Herbert's seemingly coordinated and goal-directed behaviour emerges from the interactions of about fifteen simple behaviours. Each simple behaviour is produced by a separate module, and each of these modules functions without reference to the others. (Unfortunately, Herbert's mean time from power-on to hardware failure is no more than fifteen minutes, owing principally to the effects of vibration.) Other robots produced by Brooks and his group include Genghis, a six-legged robot that walks over rough terrain and will obediently follow a human, and Squirt, which bides in dark corners until a noise beckons it out, when it will begin to follow the source of the noise, moving with what appears to be circumspection from dark spot to dark spot. Other experiments involve tiny "gnat" robots. Speaking of potential applications, Brooks describes possible colonies of gnat robots designed to inhabit the surface of TV and computer screens and keep them clean. Brooks admits that even his more complicated artificial insects come nowhere near the complexity of real insects. One question that must be faced by those working in situated AI is whether insect-level behaviour is a reasonable initial goal. John von Neumann, the computer pioneer and founder, along with Turing, of the research area now known as "artificial life", thought otherwise. In a letter to the cyberneticist Norbert Wiener in 1946, von Neumann argued that automata theorists who select the human nervous system as their model are unrealistically picking "the most complicated object under the sun", and that there is little advantage in selecting instead the ant, since any nervous system at all exhibits "exceptional complexity". Von Neumann believed that "the decisive break" is "more likely to come in another theater" and recommended attention to "organisms of the virus or bacteriophage type" which, he pointed out, are "self-reproductive and ... are able to orient themselves in an unorganised milieu, to move towards food, to appropriate it and to use it". This starting point would, as he put it, provide "a degree of complexity which is not necessarily beyond human endurance". The frame problem The products of nouvelle AI are quite different from those of symbolic AI, for example Shakey and FREDDY. These contained an internal model (or "representation") of their micro-worlds, consisting of symbolic descriptions. This structure of symbols had to be updated continuously as the robot moved or the world changed. The robots' planning programs would juggle with this huge structure of symbols until descriptions were derived of actions that would transform the current situation into the desired situation. All this computation required a large amount of processing time. This is why Shakey performed its tasks with extreme slowness, even though careful design of the robot's environment minimised the complexity of the internal model. In contrast, Brooks' robots contain no internal model of the world. Herbert, for example, continuously discards the information that is received from its sensors, sensory information persisting in the robot's memory for no more than two seconds. AI researchers call the problem of updating, searching, and otherwise manipulating, a large structure of symbols in realistic amounts of time the frame problem. The frame problem is endemic to symbolic AI. Some critics of symbolic AI believe that the frame problem is largely insolvable and so maintain that the symbolic approach will not "scale up" to yield genuinely intelligent systems. It is possible that CYC, for example, will succumb to the frame problem long before the system achieves human levels of knowledge. Nouvelle AI sidesteps the frame problem. Nouvelle systems do not contain a complicated symbolic model of their environment. Information is left "out in the world" until such time as the system needs it. A nouvelle system refers continuously to its sensors rather than to an internal model of the world: it "reads off" the external world whatever information it needs, at precisely



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) the time it needs it. As Brooks puts it, the world is its own best model--always exactly up to date and complete in every detail. Situated AI Traditional AI has by and large attempted to build disembodied intelligences whose only way of interacting with the world has been via keyboard and screen or printer. Nouvelle AI attempts to build embodied intelligences situated in the real world. Brooks quotes approvingly from the brief sketches that Turing gave in 1948 and 1950 of the "situated" approach. Turing wrote of equipping a machine "with the best sense organs that money can buy" and teaching it "to understand and speak English" by a process that would "follow the normal teaching of a child". Turing contrasted this with the approach to AI that focuses on abstract activities, such as the playing of chess. He advocated that both approaches be pursued, but until now relatively little attention has been paid to the situated approach. The situated approach is anticipated in the writings of the philosopher Bert Dreyfus, of the University of California at Berkeley. Dreyfus is probably the best-known critic of symbolic AI. He has been arguing against the Physical Symbol System Hypothesis since the early 1960s, urging the inadequacy of the view that everything relevant to intelligent behaviour can be captured by means of structures (e.g. lists) of symbolic descriptions. At the same time he has advocated an alternative view of intelligence, which stresses the need for an intelligent agent to be situated in the world, and he has emphasised the role of the body in intelligent behaviour and the importance of such basic activities as moving about in the world and dealing with obstacles. Once reviled by admirers of AI, Dreyfus is now regarded as a prophet of the situated approach. Cog Brooks' own recent work has taken the opposite direction to that proposed by von Neumann in the quotations given earlier. Brooks is pursuing AI's traditional goal of human-level intelligence, and with Lynn Andrea Stein, he has built a humanoid robot known as Cog. Cog has four microphone-type sound sensors and is provided with saccading foveated vision by cameras mounted on its "head". Cog's (legless) torso is capable of leaning and twisting. Strain gauges on the spine give Cog information about posture. Heat and current sensors on the robot's motors provide feedback concerning exertion. The arm and manipulating hand are equipped with strain gauges and heat and current sensors. Electrically-conducting rubber membranes on the hand and arm provide tactile information. Brooks believes that Cog will learn to correlate noises with visual events and to extract human voices from background noise; and that in the long run Cog will, through its interactions with its environment and with human beings, learn for itself some of the wealth of common sense knowledge that Lenat and his team are patiently hand-coding into CYC. Critics of nouvelle AI emphasis that so far the approach has failed to produce a system exhibiting anything like the complexity of behaviour found in real insects. Suggestions by some advocates of nouvelle AI that it is only a short step to systems which are conscious and which possess language seem entirely premature.

11.12 Chess Some of AI's most conspicuous successes have been in chess, its oldest area of research. In 1945 Turing predicted that computers would one day play "very good chess", an opinion echoed in 1949 by Claude Shannon of Bell Telephone Laboratories, another early theoretician of computer chess. By 1958 Simon and Newell were predicting that within ten years the world chess champion would be a computer, unless barred by the rules. Just under 40 years later, on May 11 1997, in midtown Manhattan, IBM's Deep Blue beat the reigning world champion, Gary Kasparov, in a six-game match.



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) Critics question the worth of research into computer chess. MIT linguist Noam Chomsky has said that a computer program's beating a grandmaster at chess is about as interesting as a bulldozer's "winning" an Olympic weight-lifting competition. Deep Blue is indeed a bulldozer of sorts--its 256 parallel processors enable it to examine 200 million possible moves per second and to look ahead as many as fourteen turns of play. The huge improvement in machine chess since Turing's day owes much more to advances in hardware engineering than to advances in AI. Massive increases in cpu speed and memory have meant that each generation of chess machine has been able to examine increasingly more possible moves. Turing's expectation was that chess-programming would contribute to the study of how human beings think. In fact, little or nothing about human thought processes has been learned from the series of projects that culminated in Deep Blue.

11.13 Is Strong AI Possible? The ongoing success of applied Artificial Intelligence and of cognitive simulation seems assured. However, strong AI, which aims to duplicate human intellectual abilities, remains controversial. The reputation of this area of research has been damaged over the years by exaggerated claims of success that have appeared both in the popular media and in the professional journals. At the present time, even an embodied system displaying the overall intelligence of a cockroach is proving elusive, let alone a system rivalling a human being. The difficulty of "scaling up" AI's so far relatively modest achievements cannot be overstated. Five decades of research in symbolic AI has failed to produce any firm evidence that a symbolsystem can manifest human levels of general intelligence. Critics of nouvelle AI regard as mystical the view that high-level behaviours involving language-understanding, planning, and reasoning will somehow "emerge" from the interaction of basic behaviours like obstacle avoidance, gaze control and object manipulation. Connectionists have been unable to construct working models of the nervous systems of even the simplest living things. Caenorhabditis elegans, a much-studied worm, has approximately 300 neurons, whose pattern of interconnections is perfectly known. Yet connectionist models have failed to mimic the worm's simple nervous system. The "neurons" of connectionist theory are gross oversimplifications of the real thing. However, this lack of substantial progress may simply be testimony to the difficulty of strong AI, not to its impossibility. Let me turn to the very idea of strong artificial intelligence. Can a computer possibly be intelligent, think and understand? Noam Chomsky suggests that debating this question is pointless, for it is a question of decision, not fact: decision as to whether to adopt a certain extension of common usage. There is, Chomsky claims, no factual question as to whether any such decision is right or wrong--just as there is no question as to whether our decision to say that aeroplanes fly is right, or our decision not to say that ships swim is wrong. However, Chomsky is oversimplifying matters. Of course we could, if we wished, simply decide to describe bulldozers, for instance, as things that fly. But obviously it would be misleading to do so, since bulldozers are not appropriately similar to the other things that we describe as flying. The important questions are: could it ever be appropriate to say that computers are intelligent, think, and understand, and if so, what conditions must a computer satisfy in order to be so described? Some authors offer the Turing test as a definition of intelligence: a computer is intelligent if and only if the test fails to distinguish it from a human being. However, Turing himself in fact pointed out that his test cannot provide a definition of intelligence. It is possible, he said, that a computer which ought to be described as intelligent might nevertheless fail the test because it is not capable of successfully imitating a human being. For example, why should an intelligent robot designed to oversee mining on the moon necessarily be able to pass itself off in conversation as a human being? If an intelligent entity can fail the test, then the test cannot function as a definition of intelligence.



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) It is even questionable whether a computer's passing the test would show that the computer is intelligent. In 1956 Claude Shannon and John McCarthy raised the objection to the test that it is possible in principle to design a program containing a complete set of "canned" responses to all the questions that an interrogator could possibly ask during the fixed time-span of the test. Like Parry, this machine would produce answers to the interviewer's questions by looking up appropriate responses in a giant table. This objection--which has in recent years been revived by Ned Block, Stephen White, and myself--seems to show that in principle a system with no intelligence at all could pass the Turing test. In fact AI has no real definition of intelligence to offer, not even in the sub-human case. Rats are intelligent, but what exactly must a research team achieve in order for it to be the case that the team has created an artefact as intelligent as a rat? In the absence of a reasonably precise criterion for when an artificial system counts as intelligent, there is no way of telling whether a research program that aims at producing intelligent artefacts has succeeded or failed. One result of AI's failure to produce a satisfactory criterion of when a system counts as intelligent is that whenever AI achieves one of its goals-for example, a program that can summarise newspaper articles, or beat the world chess champion--critics are able to say "That's not intelligence!" (even critics who have previously maintained that no computer could possibly do the thing in question). Marvin Minsky's response to the problem of defining intelligence is to maintain that "intelligence" is simply our name for whichever problem-solving mental processes we do not yet understand. He likens intelligence to the concept "unexplored regions of Africa": it disappears as soon as we discover it. Earlier Turing made a similar point, saying "One might be tempted to define thinking as consisting of those mental processes that we don't understan"'. However, the important problem remains of giving a clear criterion of what would count as success in strong artificial intelligence research.

11.13.1 The Chinese Room Objection One influential objection to strong AI, the Chinese room objection, originates with the philosopher John Searle. Searle claims to be able to prove that no computer program--not even a computer program from the far-distant future--could possibly think or understand. Searle's alleged proof is based on the fact that every operation that a computer is able to carry out can equally well be performed by a human being working with paper and pencil. As Turing put the point, the very function of an elektronic computer is to carry out any process that could be carried out by a human being working with paper and pencil in a "disciplined but unintelligent manner". For example, one of a computer's basic operations is to compare the binary numbers in two storage locations and to write 1 in some further storage location if the numbers are the same. A human can perfectly well do this, using pieces of paper as the storage locations. To believe that strong AI is possible is to believe that intelligence can "emerge" from long chains of basic operations each of which is as simple as this one. Given a list of the instructions making up a computer program, a human being can in principle obey each instruction using paper and pencil. This is known as "handworking" a program. Searle's Chinese room objection is as follows. Imagine that, at some stage in the future, AI researchers in, say, China announce a program that really does think and understand, or so they claim. Imagine further that in a Turing test (conducted in Chinese) the program cannot be distinguished from human beings. Searle maintains that, no matter how good the performance of the program, and no matter what algorithms and data-structures are employed in the program, it cannot in fact think and understand. This can be proved, he says, by considering an imaginary human being, who speaks no Chinese, handworking the program in a closed room. (Searle extends the argument to connectionist AI by considering not a room containing a single person but a gymnasium containing a large group of people, each one of whom simulates a single artificial neuron.) The interogator's questions, expressed in the form of Chinese ideograms, enter the room through an input slot. The human in the room--Clerk, let's say--



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Labor improbus omnia vincit (Het onvermoeid werken overwint alles) follows the instructions in the program and carries out exactly the same series of computations that an electronic computer running the program would carry out. These computations eventually produce strings of binary symbols that the program instructs Clerk to correlate, via a table, with patterns of squiggles and squoggles (actually Chinese ideograms). Clerk finally pushes copies of the ideograms through an output slot. As far as the waiting interrogator is concerned, the ideograms form an intelligent response to the question that was posed. But as far as Clerk is concerned, the output is just squiggles and squoggles--hard won, but completely meaningless. Clerk does not even know that the inputs and outputs are linguistic expressions. Yet Clerk has done everything that a computer running the program would do. It surely follows, says Searle, that since Clerk does not understand the input and the output after working through the program, then nor does an electronic computer. Few accept Searle's objection, but there is little agreement as to exactly what is wrong with it. My own response to Searle, known as the Logical Reply to the Chinese room objection, is this. The fact that Clerk says "No" when asked whether he understands the Chinese input and output by no means shows that the wider system of which Clerk is a part does not understand Chinese. The wider system consists of Clerk, the program, quantities of data (such as the table correlating binary code with ideograms), the input and output slots, the paper memory store, and so forth. Clerk is just a cog in a wider machine. Searle's claim is that the statement "The system as a whole does not understand" follows logically from the statement "Clerk does not understand". The logical reply holds that this claim is fallacious, for just the same reason that it would be fallacious to claim that the statement "The organisation of which Clerk is a part has no taxable assets in Japan" follows logically from the statement "Clerk has no taxable assets in Japan". If the logical reply is correct then Searle's objection to strong AI proves nothing.


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