Les trois générations de tuteur

1. Les trois générations de tuteur Roger Nkambou

2. 3 Générations de tuteurs • 1ère Génération– Sur le marché • Technologie sous-jacente : Hypertexte & Behaviorisme • Pédagogie: Feedback didactique sur les réponses de l’apprenant. • 2ème Génération – Emergent sur le marché • Technologie : Intelligence Artificielle & Psychologie Cognitive • Pédagogie : Assistance sur les étapes de résolution d’un problème (et non seulement sur une réponse finale) • 3ème Génération– Emergent dans les labos • Technologie : Traitement du langage naturelle, plannification réactive, évaluation du continue de la pédagogie et du contenu • Pédagogie : Dialogues permettant la construction des connaissances

3. La 1ère génération:EAO (Enseignement Assisté par Ordinateur) (CAI – Computer-Based Instruction) • Exemple Excellent! OK Solve 2+2x=12 Multiplication has a higher precedence than addition, so 2+2x is the same as 2+(2x), not (2+2)x. Try again. x=5 x=3 x=7 OK

4. La 1ère génération (suite) Exemple 2:

5. La boucle fonctionnelle des EAO Pour chaque chapitre du curriculum • Lire le chapitre • Pour chaque exercice • Boucle • Prendre la réponse • Donner la rétroaction et les conseils sur la réponse • Sortir si bonne réponse • Essayez de nouveau • Fin boucle • FinPour • Passer un test sur le chapitre FinPour

6. Student’s workspace: 2 + 2x = 12 4x = 12 2x = 12 - 2 Hint Tutor: Good! 2ème génération:Systèmes Tutoriels Intelligents Classiques • Technologie sous-jacente: IA et Psycho. Cogn. • Pédagogie: Assistance sur les étapes d’un problème • Exemple: • Tutor: Solve 2+2x=12 • Student: <enters 4x=12> • Tutor: Not quite. Try again. • Student: <clicks on “hint” button> • Tutor: Think about operator precedence. • Student: <enters 2x=12-2> • Tutor: Good!

7. La boucle fonctionnelle des STI classiques Pour chaque chapitre du curriculum • Lire le chapitre • Pour chaque exercice • Boucle • Pour chaque étape de l’exercice • Boucle • Prendre la réponse • Donner la rétroaction et les conseils sur la réponse • Sortir si bonne réponse • Essayez de nouveau • Fin boucle • FinPour • FinPour • Passer un test sur le chapitre FinPour

8. 2+2x=12 4x=12 3ème génération • Technologie: Planification réactive & Traitement du langage naturel • Pedagogie: Dialogues visant la construction des connaissances • Exemple: • Tutor: Solve 2+2x=12 • Student: 4x=12 • Tutor: Should this equation have the same solution as the first one? • Student: Yes. • Tutor: The solution to 4x=12 is 3, so let’s check for an error by trying x=3 in 2+2x=12. • Student: 2+2*3=2+6=8 oops! • Tutor: Right! Now look at the arithmetic steps you did … Student’s workspace: Hint Dialog: S: 2+2*3=2+6=8 oops! T: Right! Now look...

9. Exempe: Algebra Cognitive Tutor

10. Boucle fonctionnelle des tuteurs de 3ème génération Pour chaque chapitre du curriculum • Lire le chapitre • Pour chaque exercice • Boucle • Pour chaque étape de l’exercice • Boucle • Prendre la réponse • Sortir si bonne réponse • Pour chaque inférence en relation avec le bon raisonnement - Eliciter cette inférence chez l’apprenant - Conseiller, ‘prompter’ - Sortir si l’étudiant complète l’étape • FinPour • Fin Boucle • FinPour • FinPour • Passer un test sur le chapitre FinPour

11. Limites des tuteurs de 2ème génération • Beaucoup moins bons que les tuteurs humains! • Ne permettent pas toujours une compréhension profonde de la matière • Les symptômes d’un apprentissage superficiel: • Peu de transfert de K dans de nouvelles situation de résolution de problèmes • Peu d’habilité à expliquer (via une conversation abstraite cohérente sur le domaine)

12. Les tuteurs de 3e génération Dialogues pour la construction de connaissances “there is something about conversational dialog that plays an important role in learning”. Meilleure théorie sur la stratégie tutorielle visant à promouvoir l’apprentissage : “Good tutors tell less and ask more.” • Ils guident les étudiants au fil de leur processus de construction de nouvelles connaissances. • Ils les aide à faire des abstractions • Ils les aide à créer des connections qui aide au transfert.

13. L’orientation des recherches sur les T3G • Sur le plan empirique • Déterminer QUAND et POURQUOI le dialogue tutorielle est éfficace et utile. • Sur le plan technique • Développer des systèmes qui supportent les apprenants dans la construction de connaissances à travers le dialogue tutoriel • Evaluater l’efficacité de ces systèmes • Le but est de rivaliser ou ‘surpasser’ l’efficacité des tuteurs humains

14. Exemples de T3G • Andes/Atlas: Dialogue plutôt que Conseil • Why/Atlas: Dialogues critiques • CIRCSIM: Dialogue dans le but de corriger les erreurs dans les prédictions des étudiants sur la causalité physiologique • AutoTutor: Dialogue sur le domaine des ordi • Geometry Explanation Tutor : Dialogue pour la résolution de problème en géométrie. • Ms. Lindquist: Dialogue concernant les méthodes pour l’analyse des mots algébriques

15. Andes/Atlas: Le dialogue remplace les séquences de conseils Andes: If you are moving in a straight line and accelerate in the same direction, does your velocity increase or decrease? You: increase Andes: You’ve drawn the acceleration of the elevator in the same direction as the velocity. Is the velocity of the elevator increasing?

16. Why/Atlas

17. CIRCSIMMartha Evens, Reva Freedman, Michael Glass, Yujian Zou, et al., Illinois Institute of Technology • Domaine: physiologie (contrôle de la pression sanguine) • Emphase: dialogue dans le but de corriger les erreurs de prédictions des étudiants sur la cautsalité physiologique • Questions à réponses courtes, stratégies de conseil

18. CIRCSIM-Tutor (Interface usager) Problem: Pacemaker malfunctions, increasing to 120 beats/min. T> What variable is affected by HR? S> Cardiac Output. T> But you predicted that HR increases and CO decreases. S>

19. Modèle causal dans CIRCSIM

20. Famille de dialogues possible dans CIRCSIM

21. AutoTutorArt Graesser, Derek Harter, Peter Wiemer-Hastings, Natalie Person, et al., University of Memphis • Domaine: Architecture des ordinateurs • Emphase: Simuler le tuteur humain moyen • Dialogue à initiative mixte comportant des questions permettant le raisonnement profond • Interface: Face parlante avec gestures et intonation • Flot du dialogue • Prompt, Conseil, Assertion • Rétroaction • Back channeling • Feedback pédagogique (évaluation des réponses) • Feedback correctif (remédiatif) • Répond aux initiatives de l’étudiant

22. AutoTutor avec la tête parlante Marco

23. TUTOR-1: Alright, let's go on. You want to take photos and send them over the Internet. Consider this problem. What hardware do you need to take photos and send them over the Internet? STUDENT-1: First of all you need to have access to the internet TUTOR-2: What else? STUDENT-2: a camera TUTOR-3: Yeah. For what type of connection do you need a network card? STUDENT-3: network card TUTOR-4: Okay. Now, if you have access to the Internet through a network card, then your connection is … STUDENT-4: I don't know. TUTOR-5: Is direct. Okay. A network card is needed if you have a direct connection to the Internet. Okay. How does the user get hooked up to the internet? STUDENT-5: by a plug. TUTOR-6: Once again, a network card is needed if you have a direct connection to the Internet. Okay, what does a modem do? STUDENT-6: connects network card and internet TUTOR-7: Okay. Try this. A modem is required when there is … STUDENT-7: a connection TUTOR-8: a dial up connection. ... TUTOR-30: Let’s review. To send your photos on the Internet, you need either a digital camera or a regular camera to take the photos. If you use a regular camera, you need a scanner to scan them onto a computer disk. If you have a direct connection to the Internet, then you need a network card. A modem is needed if you have a dial up connection. Un exemple de dialogue tutoriel dans AutoTutor

24. Geometry Explanation Tutor • But: Aider les étudiants à apprendre via un tutorat par explication Allant de : l’aprentissage par la pratique A: l’apprentissage par la pratique et l’explication • Motivé par les recherches en Sciences Cognitives sur le “Self-Explanation Effect” • Comment permettre aux tuteurs de mieux supporter l’Auto-Explication ? • Les étudiants expliquent-ils avec leurs propres mots ? • Est-il nécessaire de donner une rétroaction sur les explications ?

25. Étude 1: Explication ‘par référence’ Le feedback du tuteur indique si l’explication est correcte.

26. Étude 2:Et l’auto-explication sans rétroaction du tuetur sur les explications de l’étudiant?

27. Les étudiants produisent très peu de bonnes explications... Hypothèse: Les étudiants apprennent mieux lorsqu’ils expliquent dans leurs propres mots et le tuteur les aide à rafiner leurs explications par le doalogue.

28. Un dialogue tutoriel pour supporter l’auto-explication dans GeometryExplanationTutor

29. Exemple de dialogue dans GeometryExplanationTutor Student:angles are equal Tutor: Are any two angles congruent? What tells you that there are two congruent angles here? Student: angles in a triangle are equal Tutor: That is a bit imprecise. Are ALL angles in ALL triangles congruent? Student: angles in an isosceles triangle are equal Tutor: Correct, except for one thing: are all three angles in an isosceles triangle congruent? Student:base angles in an isosceles triangle are equal Tutor: CORRECT

30. Architecture de GeometryExplanationTutor STATISTICAL CLASSIFIER INTERFACE USAGER Feedback or Help Message (Numerical)Answer or Hint Request MODULE DE COMPRÉHENSION LA LN Student Explanation STUDENT MODEL Ballpark Classification of Explanation LCFLEX PARSER GRAMMAR & LEXICON PRODUCTION ENGINE FEATURE STRUCTURE UNIFIER FEATURE STRUCTURES COGNITIVE MODEL KNOWLEDGE BASE —Ontology & Explanation Hierarchy LOGIC SYSTEM (Loom) SEMANTIC REPRESENTATION of Explanation Detailed Classification of Explanation TUTEUR COGNIF

31. Connaissances pédagogiques: Hiérarchie d’explication

32. Exemple d’hiérarchie partielle pour l’explication du théorème des triangles isocèles UNKNOWN CONGR-ANGLES“The angles are congruent.” BASE-ANGLES“These are base angles.” OPPOSITE-ANGLES“Opposite angles are congruent.” CONGR-ANGLES-IN-TRI“Angles in a triangle are congruent.” BASE-ANGLES-CONG“Base angles are congruent.” ANGLES-OPP-SIDES“Angles opposite the sides are congruent.” CONGR-ANGLES-IN-ISOS-TRI“Angles of an isosceles triangle are congruent.” TRI-BASE-ANGLES“Base angles in a triangle are congruent.” ANGLES-OPP-CONGR-SIDES“Angles opposite congruent sides are congruent.” ISOS-TRI-BASE-ANGLES“Base angles in an isosceles triangle are congruent.” ISOS-TRIANGLE“The angles opposite congruent sides in an isosceles triangle are congruent.”

33. L’auto-explication en langage naturel améliore l’apprentissage car : • “There is something about NL dialog that is right ...” • Le langage naturel est naturel pour l’apprenant • Il est bien pour les étudiants d’expliquer en leurs propres mots… Pouquoi donc ? • L’explication en LN nécessite la rétention (le rappel) plutôt que la reconnaissance (CONT. CHI) • L’articulation force l’attention sur les facteurs pertinents • L’usage du verbal et du visuel crée une dualité en mémoire. • Le LN permet une flexibilité dans l’expression des connaissances partielles • Les étudiants peuvent montrer ce qu’ils savent • Le tuteur peut les aider à construire ce qu’ils ne savent pas • L’aide peut être incrémentale • Le tuteur peut supporter plusieurs chemins de construction de connaissances

34. Un autre cas : DIALOGUE INTERACTIF POUR DES FINS DE REFLEXION • Le dialogue interactif dans ce contexte dépend de : • (1) The goal of the diagnosis (deeper understanding (includes justified correction of an error), knowledge construction) • (2) The nature of the skill (Concept, Principle, Law, etc./Basic, non Basic) • Example of IDP for the principle related to a variable that is bound to a constant in the domain of Prolog Programming • Skill: Principle If an element E is a Prolog Variable & this element is associated with a constant value V Then E can only be associated with a value equivalent to V in the same context (same Prolog command) • (1) Generic Model of IDP based on the goal • (1.1) The goal is deeper understanding? • Articulate the features of the problem which elicit the skills (Implicit reflection) • (2) The goal is knowledge construction? • Instantiate the 5 stages of explicit reflective thinking as defined by Dewey (Dewey 1933) in the context of the nature of the skill

35. Plan de dialogue 1 • Research Background & Rationale • Goals • Generic Models & Challenges • Implementation • Related Work • Evaluation & Future Work Start Done Tutor presents a problem • Types of remediation targeted through interactive diagnosis • General comprehension • Deep comprehension • Knowledge construction Student Gives final answer/ performs next action, Asks for help Is the answer correct ? YES NO Tutor gives negative feedback Tutor takes the INTERACTIVE-DIAGNOSIS PLAN for the skill associated with the problem There are no more interactions Interactive diagnosis from which a general comprehension of the skills associated with a problem is expected trough implicit reflective thinking ((Flavell 1979, Hartman 2001) Tutor Triggers the NEXT interaction in the CURRENT diagnosis plan Is the learner’s answers during the interaction correct? YES NO Tutor sends a positive evidence to the learner model, for the skill associated with that interaction Tutor sends a negative evidence to the learner model, for the skill associated with that interaction Hypothesis (2) Tutor explains the skill associated with the interaction Hypothesis (1)

36. Plan de dialogue 2 Start Done + Show Skills tracing • Types of remediation targeted through interactive diagnosis • General comprehension • Deep comprehension • Knowledge construction Tutor presents a problem Student Gives final answer/ performs next action, Asks for help Generate an Interactive Diagnosis PLAN to verify comprehension YES Is the answer correct ? NO Tutor gives negative feedback Hypothesis (1,2,3) Tutor takes the INTERACTIVE-DIAGNOSIS PLAN for the skill associated with the problem There are no more interactions Interactive diagnosis from which a deep comprehension of the skills associated with a problem is expected through implicit reflective thinking (Flavell 1979, Hartman 2001) Tutor triggers the NEXT interaction with the learner in the CURRENT diagnosis PLAN Are the learner’s answers during an interaction correct? The Skill associated with the interaction is Sk(i) YES NO Tutor sends a positive evidence to the learner model, for Sk(i) Tutor sends a negative evidence to the learner model, for Sk(i) Hypothesis (2) Is Sk(i) a Basic Skill (does not necessitate the ellicitation of another intellectual skill) Hypothesis (1) and Hypothesis (3) NO YES Tutor takes an INTERACTIVE-Diagnosis Plan for Sk(i) Tutor articulates Sk(i)

37. Plan de dialogue 3 Start Done + Show Skills tracing • Types of remediation targeted through interactive diagnosis • General comprehension • Deep comprehension • Knowledge construction Tutor Challenges the student with two (or more) situations that are contradictory and asks the learner to make an appropriate inference Student Proposes an inference Tutor takes the INTERACTIVE-DIAGNOSIS PLAN for the skills associated with the target Inference There are no more interactions Interactive diagnosis from which knowledge construction is expected through explicit reflective thinking as in Dewey (Dewey, 1933) Tutor triggers the NEXT interaction with the learner in the CURRENT diagnosis PLAN: Asks the learner to link an observation with a principle or the contradiction of a principle, rules, law: Sk(i)(Depending of the inference that he drew) Has the student made a correct link? YES NO Tutor sends a positive evidence to the learner model, for Sk(i) Tutor sends a negative evidence to the learner model, for Sk(i) Hypothesis (2) Tutor explains Sk(i) by outlining its use in 2 conflicting situations (Dewey, 1933) Hypothesis (1) To implement Hypothesis (3): Challenges the learner in that specific skill

38. Exemple de dialogue… • ?- X= 37, Y= Z, Z=10, X=Y • [T1] This command will provide the result “Fail” in Prolog while • [?- X= 37, Y= Z, X=Y • will result in “Success” IDP for deeper understanding (non basic skill) • Tutor tries to outline a situation where a conflict occurs • Tutor fosters the learner towards the induction of a principle, fact, procedure • [T4] What are the variables in this command? • [S5] X,Y,Z [Diagnosis: Students knows <identify a Variable>] • [T6] Which variables are associated to constants in the first command? • [S7] X,Z • [T8] Hummm, not really, which variable is associated with Z? [Diagnosis: Students knows <apply transitive binding of Variables>] • [S9] Y • ..... • ?- X= 37, Y= Z, Z=10, X=Y • [T1] What is the result of this command? • [L2] Success • [T3] Hummmm, no not really IDP for deeper understanding (non basic skill) • Tutor tries to outline the conditions of the principle in this particular context • Tutor brings thelearner to a contradiction of the principle • [T4] What are the variables in this command? • [S5] X,Y,Z [Diagnosis: Students knows <identify a Variable>] • [T6] Right. Is there a constant value associated with X (If yes, give it) • [S7] 37 • [T8] What are the other elements of the command associated with X? • [S9] Y • .....

39. Implémentation dans Prolog-Tutor • Prolog-Tutor: Logic Programming Concepts • Skills of the domain in this context • Correctly identify and Use of basic structures (variabgles, constant, compound terms, facts, Prolog-rules, etc.) • Understand and apply the principle related to binding a Variable (Understand and perform Unification(1)) • Understand Unification (Concept); Perform unification (Procedure) • Understand Resolution (Concept). Perform resolution (procedure)

40. Learner model = Skills Domain Knowledge Elements

41. Implementation (2) • Prolog-Tutor: Logic Programming Concepts • Teaching: an example with RESOLUTION as a procedure (the learner has to perform it) • Dialogue initial plan: All the steps of the procedure • Dialogue utterances: • Tutor question: What the learner should do at that step? • [Expected reflection: recall, organize, test the skills necessary at each step: Principles to apply, Concepts and Facts which define the conditions of the problem state and which will be used to test a principle] • [Expected remedial state: deeper understanding, correction] • Dialogue management: (see the paper of this workshop “Elaborating …”) • The initial dialogue plan may be adapted, when the learner model used • The discourse may be accommodated or elaborated when the adapted dialogue reflects some irregularities

42. Learner model: Skills of the domain with associated probabilities (Simulated) Interactions: Tutor: Asks question to the learner in each step of the procedure after that he has failed to answer to a question Application of Hypothesis (2) Diagnosis in Background: if the learner is unable to answer the question “What is the goal to prove”, the Tutor diagnoses the skill “Understand the meaning of a GOAL in resolution” Application of Hypothesis(1) Expectation of reflection when the tutor asks: “ What is the GOAL to prove in this problem”: Recall: What is the purpose of a RESOLUTION? What is a GOAL in a resolution? What is the role of Knowledge Base? Explanation after failure to answer to a question during an interaction Interactive Diagnosis in Prolog-Tutor (Scenario of Slide 10) Reflection Elicited: Implicit reflection through the articulation of a procedure Enhanced or remedial cognitive state expected: General understanding of the skill (Apply a Resolution or Procedure of Resolution) (Slide 10)

43. Skills Traced by the tutor (at the end of all scenarios) Reflection Elicited: Implicit reflection through showing to the learner his cognitive state as viewed by the system in terms of skills (we should add the interaction which justifies the inference of that cognitive state) Enhanced or remedial cognitive state expected: General understanding of the elements of the domain Skills Tracing allows the tutor to generate an exercise for a specific skill, when the learner request it

44. Conclusion • Les 3 générations de tuteurs diffèrent par • Leur technologie sous-jacente • Leur pédagogie • Et les approches pour leur développement • Un apprentissage superficiel (non profond) peut survenir lorsque l’étudiant n’a pas encoder les aspects pertinents de la tâche • CIRSIM, AutoTutor, Ms. Lindquist, et Geometry Explanation Tutor sont des exemples de T3G (Tuteurs cognitifs), Prolog-Tutor aussi. • Intuitivement, le dialogue en langage naturel parraît très efficace et utile dans l’apprentissage mais il reste à déterminer (par la recherche) le QUAND et le COMMENT.