Le tutorat 3 Générations de tuteurs

Transcription

Le tutorat
Roger Nkambou
Roger Nkambou, DIC9340
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
1
La 1ère génération:
EAO (Enseignement Assisté par
Ordinateur) (CAI – Computer-Based Instruction)
Exemple
Excellent!
OK
Solve 2+2x=12
x=5
x=3
x=7
Multiplication has a
higher precedence
than addition, so 2+2x
is the same as 2+(2x),
not (2+2)x. Try again.
OK
La 1ère génération (suite)
Exemple 2:
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
FinPour
2ème génération:
Systèmes Tutoriels Intelligents Classiques
Technologie sous-jacente: IA et Psycho. Cogn.
Pédagogie: Assistance sur les étapes d’un problème
Exemple:
Student’s workspace:
Tutor:
Solve 2+2x=12
2 + 2x = 12
Student: <enters 4x=12>
4x = 12
Tutor:
Not quite. Try again.
2x
= 12 - 2
Student: <clicks on “hint” button>
Hint

Tutor:
Think about operator
precedence.
Student: <enters 2x=12-2>
Tutor:
Good!
Tutor:
Good!
3
La boucle fonctionnelle des STI
classiques
Lire le chapitre

Boucle
Pour chaque étape de l’exercice
Boucle

Prendre la réponse
Donner la rétroaction et les conseils sur la réponse
Essayez de nouveau
Fin boucle
FinPour
FinPour
FinPour
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:
2+2x=12
4x=12
Hint
Dialog:
S: 2+2*3=2+6=8
oops!
T: Right! Now look...
4
Exempe: Algebra Cognitive Tutor
Boucle fonctionnelle des
tuteurs de 3ème génération
Lire le chapitre

Boucle
Pour chaque étape de l’exercice
Boucle

Prendre la 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
FinPour
5
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.
6
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
7
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
8
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.
DR RR SS
Central Venous Pressure
Inotropic State
Stroke Volume
Heart Rate
Cardiac Output
Total Peripheral Resistance
Mean Arterial Pressure
0
+
0
+
T> What variable is affected by
HR?
S> Cardiac Output.
T> But you predicted that HR
increases and CO decreases.
S>
9
Modèle causal
dans CIRCSIM
Transfusion
(or Hemorrhage)
+
Central
Venous
Pressure
+
+
Central Blood
Volume
Blood Volume
−
−
+
Stroke
Volume
Mean
Arterial
Pressure
+
Cardiac
Output
+
+
+
+
−
Sino-Atrial
Node
Rate
+
Baroreceptor
Pressure
Total
Peripheral
Resistance
Heart
Rate
Intracellular
Ca++
Concentration
+
+
+
Inotropic
State
−
+
Arteriolar
Resistance
−
−
Arteriolar
Diameter
+
Nervous
System
Response
−
Arteriolar
Muscle
Tone
−
Beta-Blocker
(drug)
−
Pacemaker Rate Up
(or Down)
Alpha-Blocker
(drug)
Famille de dialogues possible dans CIRCSIM
(1 ) Ca n yo u te ll m e wh a t co n tro ls TPR?
Nerv ou s
sy s t em
Rig h t
Sy m p a t h et ic
va socon st riction
Rig h t.
TPR is
n e u ra l.
Ra d iu s of
a rt erioles
I have
n o id ea
An d wh a t
co n tro ls
t h a t?
TPR is
n e u ra lly
co n tro lle d
Nerv ou s
sys t em
Rig h t
<w r on g >
POP
(2 ) Re m e m b e r t h a t we ’re in DR n o w.
(3 ) So wh a t m u s t b e th e va lu e o f TPR?
10
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
11
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 ?
12
Étude 1: Explication ‘par référence’
Le feedback du tuteur
indique si l’explication
est correcte.
Étude 2:
Et l’autoexplication
sans
rétroaction du
tuetur sur les
explications
de l’étudiant?
13
Les étudiants produisent très
peu de bonnes explications...
Correct Explanations
9%
Partially-Correct or Incorrect Explanations
26%
Non-Attempts
64%
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’autoexplication dans GeometryExplanationTutor
14
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
Architecture de GeometryExplanationTutor
INTERFACE USAGER
Feedback or
Help Message
STUDENT
MODEL
(Numerical)
Answer or
Hint Request
Ballpark
Classification of
Explanation
Student
Explanation
STATISTICAL
CLASSIFIER
LCFLEX
PARSER
PRODUCTION
ENGINE
FEATURE
STRUCTURES
COGNITIVE
MODEL
TUTEUR
COGNIF
Detailed
Classification of
Explanation
MODULE DE
COMPRÉHENSION
LA LN
SEMANTIC
REPRESENTATION
of Explanation
GRAMMAR &
LEXICON
FEATURE
STRUCTURE
UNIFIER
LOGIC
SYSTEM
(Loom)
KNOWLEDGE
BASE —Ontology
& Explanation
Hierarchy
15
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.”
BASE-ANGLES-CONG
“Base angles are
congruent.”
TRI-BASE-ANGLES
“Base angles in a triangle are
congruent.”
CONGR-ANGLES-IN-TRI
“Angles in a triangle are
congruent.”
CONGR-ANGLES-IN-ISOSTRI
“Angles of an isosceles triangle
are congruent.”
ISOS-TRI-BASE-ANGLES
“Base angles in an isosceles
triangle are congruent.”
OPPOSITE-ANGLES
“Opposite angles are
congruent.”
ANGLES-OPP-SIDES
“Angles opposite the sides are
congruent.”
ANGLES-OPP-CONGR-SIDES
“Angles opposite congruent
sides are congruent.”
ISOS-TRIANGLE
“The angles opposite congruent sides in an
isosceles triangle are congruent.”
16
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)
(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
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)

17
Start 1
Plan de dialogue
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
Student Gives final answer/ performs next
action, Asks for help
YES
Is the answer correct ?
-General comprehension
-Deep comprehension
NO
Tutor gives negative feedback
-Knowledge construction
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
Tutor sends a positive evidence to the learner
model, for the skill associated with that interaction
NO
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
Start 2
Done + Show
Skills tracing
Tutor presents a problem
Student Gives final answer/ performs next
action, Asks for help
YES
Is the answer correct ?
NO
Tutor gives negative feedback
Generate an Interactive
Diagnosis PLAN to verify
comprehension
-Deep comprehension
Hypothesis (1,2,3)
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
Are the learner’s answers during an interaction correct? The Skill associated with
the interaction is Sk(i)
YES
NO
model, for Sk(i)
diagnosis
Interactive diagnosis from which a
deep comprehension of the skills
associated with a problem is
expected through implicit reflective
thinking (Flavell 1979, Hartman 2001)
model, for Sk(i)
Is Sk(i) a Basic Skill (does not necessitate the
ellicitation of another intellectual skill)
NO
YES
Tutor articulates Sk(i)
Hypothesis (2)
Hypothesis (1) and Hypothesis (3)
Tutor takes an INTERACTIVE-Diagnosis Plan for Sk(i)
18
Plan de dialogue 3
Start
Tutor Challenges the student with two (or more)
situations that are contradictory and asks the
learner to make an appropriate inference
Done + Show Skills
tracing
diagnosis
-Deep comprehension
Student Proposes an inference
PLAN for the skills associated with the target
Inference
There are no more
interactions
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)
Interactive diagnosis from which
knowledge construction is expected
through explicit reflective thinking as in
Dewey (Dewey, 1933)
Has the student made a correct link?
YES
model, for Sk(i)
NO
model, for Sk(i)
Tutor explains Sk(i) by outlining its use in 2
conflicting situations (Dewey, 1933)
Hypothesis (2)
Hypothesis (1)
To implement Hypothesis (3):
Challenges the learner in that specific
skill
Exemple de dialogue…
?- 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
.....
19
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)
Learner model = Skills
Domain Knowledge Elements
20
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
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
Explanation after failure to
answer to a question during an
interaction
Application of Hypothesis (2)
Application of Hypothesis(1)
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”
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?
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)
21
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.
22
Autres approches de tutorat:
intégration du CBR
ELABORER
RETROUVER
PROBLEME
Cas
cible
Cas
appris
MEMORISER
Peut se faire
Partout ou le
Raisonnement
Est nécessaire:
-Planification
-Coaching
-Tutorat…
Cas cible
adapté,
évalué,
corrigé
Cas
Cas
Source Cas
Source
Base de
cas
cible
Connaissance
générale
ADAPTER
Cas cible
adapté
Solution confirmée
REVISER
Cas de la planification
Planification à base de règles

Entrée= but, situation initiale, actions
Les plans sont toujours générés par la recherche
Planification à base de cas

Retrouve les plans ayant le couple (but, situation
initiale) similaire
Ré-utilise les parties des anciens plans et les
complète
Les ajuste jusqu’à ce qu’ils satisfont au couple
(but, situation initiale)
23
Planification à base de cas
Partir du de la description du couple {But,État initial} pour
Lequel on recherche un plan désiré
Trouver un plan proche et l’adapter
Espace des Instances {But,EI}
Espace des plans
Planification dans les SABC
Vassileva & Watson (1997)
Nkambou & Kabanza (2001)
24
IMS-Simple Sequence
Arbre d’activités
Chaque activité contient un ensemble
de comportement de séquencement
IMS-Simple Sequence (suite)
25
Cadres tutoriels offerts par
IMS-LD – Quelques use-cases
Adapting Units of Learning to Learner
Profile
Obtaining Culturally Relevant Content
for Problem-Solving
Provide Remedial Units of Learning
A Problem-Based Learning Task for
Information Sciences and Technology
Using Virtual Labs
Adaptive Learning Delivery
Exemple:
Roger
Nkambou, DIC9340
Provide Remedial Units of
Learning
Primary Actors: Learner
Stakeholders and Interests:
Learner - receives instruction specific to deficits in required knowledge and skill, and relevant to individual
learning needs.
Instructor - enables participation of students capable of functioning well in large-lecture course.
Organization - increases efficiency and effectiveness of introductory courses.
Preconditions:
1. The Learner has been registered with the system and has a learning profile.
2. The Learner has been registered for a qualifying course.
Trigger: The Learner attempts to log in to the qualifying course for the first time.
Main Success Scenario:
1. Learner logs into the System using an assigned id and password.
2. System recognizes the Learner, retrieves the correct profile, and offers the Learner a menu of options,
based on access authorizations.
3. Learner makes a selection that corresponds to initiation of a qualifying course.
4. System notifies student that a pre-assessment is a course requirement and prompts the Learner for a
decision whether or not to take the pre-assessment at this time.
5. Learner opts to take the pre-assessment.
6. System delivers the pre-assessment.
7. Learner takes the pre-assessment and submits the result to the system for grading.
8. Learner scores the pre-assessment, records the results, updates the learner's profile, and searches for
learning activities that address those areas below criteria.
9. System assembles a unit of learning for course remediation, based on the deficiencies uncovered by the
pre-assessment and activities aligned with the learner's profile. The unit of learning consists of a set of
activity-structures whose sequence is based on the sequence of topics in the qualifying course. Each
activity-structure contains a post-test used to verify effective completion of the activity-structure.
10. The Learner completes each activity-structure in order, takes the associated post-test, and submits the
results to the system.
11. The System records the results, grades the post-test, and updates the learner's profile.
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Le tutorat 3 Générations de tuteurs

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