Les trois générations de tuteur Roger Nkambou

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

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

La 1ère génération (suite) Exemple 2:

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

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! 2 + 2x = 12 4x = 12 2x = 12 - 2 Student’s workspace: Tutor: Good! Hint

La boucle fonctionnelle des STI classiques Pour chaque chapitre du curriculum Lire le chapitre Pour chaque exercice Boucle Pour chaque étape de l’exercice 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

3ème génération Student’s workspace: 2+2x=12 4x=12 Hint Dialog: 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: 2+2x=12 4x=12 Hint Dialog: S: 2+2*3=2+6=8 oops! T: Right! Now look...

Exempe: Algebra Cognitive Tutor

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 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 Passer un test sur le chapitre

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)

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.

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

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

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?

Why/Atlas This is a human tutor interacting over the net with a human student. This is the student’s screen. The tutor’s screen is similar. WHY will replace the human tutor. Explain each pane. There are 3 things wrong with this essay: The contradiction between first and second sentence of the essay. – need to ask which the student means The misunderstanding of the final velocity question – need to reask The lack of explanation of the answers. – need to pump the student for more Tutor picks #2 to focus on. Can shallow-linguistics approaches really understand the student’s answers well enough to know that the student has misunderstood the question twice? Note the student initiative in the current utterance.

CIRCSIM Martha Evens, Reva Freedman, Michael Glass, Yujian Zou, et al CIRCSIM Martha 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

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>

Modèle causal dans CIRCSIM

Famille de dialogues possible dans CIRCSIM

AutoTutor Art 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

AutoTutor avec la tête parlante Marco

Un exemple de dialogue tutoriel dans AutoTutor 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.

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 ? Student learn more from examples, textbook text, problem-solving, when they explain to themselves. How to get students to self-explain? Prompting or instruction helps Even so, few students generate effective self-explanations

Étude 1: Explication ‘par référence’ Mention Conati and VanLehn Le feedback du tuteur indique si l’explication est correcte.

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

Les étudiants produisent très peu de bonnes explications... If there is value in having students explain in their own words, it seems unlikely that this will be obtained without providing feedback on student explanations. Hypothèse: Les étudiants apprennent mieux lorsqu’ils expliquent dans leurs propres mots et le tuteur les aide à rafiner leurs explications par le doalogue.

Un dialogue tutoriel pour supporter l’auto-explication dans GeometryExplanationTutor Or demo? “Face lift” Pedagogical approach: support incremental construction of explanations Main thing: students explain in their own words - and now need to provide a complete explanation Tutor provides feedback (one explanation accepted)

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

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

Connaissances pédagogiques: Hiérarchie d’explication

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.”

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

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

Plan de dialogue 1 Start Research Background & Rationale Goals Generic Models & Challenges Implementation Related Work Evaluation & Future Work 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 Tutor Triggers the NEXT interaction in the CURRENT diagnosis plan Interactive diagnosis from which a general comprehension of the skills associated with a problem is expected trough implicit reflective thinking ((Flavell 1979, Hartman 2001) 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)

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 Tutor triggers the NEXT interaction with the learner in the CURRENT diagnosis PLAN Interactive diagnosis from which a deep comprehension of the skills associated with a problem is expected through implicit reflective thinking (Flavell 1979, Hartman 2001) 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) YES NO Hypothesis (1) and Hypothesis (3) Tutor takes an INTERACTIVE-Diagnosis Plan for Sk(i) Tutor articulates Sk(i)

Plan de dialogue 3 Done + Show Skills tracing 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

Exemple de dialogue… IDP for deeper understanding (non basic skill) ?- 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 ..... ?- 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 .....

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)

Domain Knowledge Elements Learner model = Skills Domain Knowledge Elements

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

Application of Hypothesis (2) 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)

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

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.