toward a fully automatic learner model based on web usage...
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Toward a fully automatic learner model based on web usage mining with respect to educational preferences and learning styles
Mohamed Koutheaïr KHRIBI University of Tunis
LaTICE, University of Tunis ICALT 2013, Beijing, China.
Mohamed JEMNI University of Tunis
Olfa NASRAOUI
University of Louisville [email protected]
Sabine GRAF Athabasca University
Kinshuk
Athabasca University [email protected]
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- Advantages of automatic learner modeling : No additional work for learners ;
Uses information from a time span; higher tolerance
Allows dynamic updating of information
ICALT 2013, Beijing, China 15/07/2013
Learner Modelling Approaches
Collaborative Automatic
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Recommendation (content, collaborative, hybrid)
LO Features Learner Features
LOi
LOn
LO2
LO1
(Learners) (Learning Objects)
1 2
3
Learner- Object LO Interest Measures
(Past& Recent)
Implicit Usage Data
Learner Modeling
Cont
ent M
odel
ing
ICALT 2013, Beijing, China 15/07/2013
General Aim Building an automatic recommendation system for Learning Management Systems.
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Research Question How to automatically model learners and groups of learners based on implicit data from their interactions and online activities in the learning management system, taking into account the learners’ * educational preferences * learning styles
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General Features of the Proposed Approach Automatic, dynamic and based solely on learner usage
sessions ;
Input data is composed from collected implicit data tracked
and saved in LMS database and/or web server log files ;
Based on web usage mining techniques ;
Educational preferences and learning styles are
considered and identified automatically.
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P
ropo
sed
Lear
ner m
odel
com
pone
nts
:
LMi = {PRi, LKi, LEPi} PRi : ith Learner Profile General student information such as Identification
data, Demographic information, etc.
LKi : ith Learner’s Knowledge
Component
Sequences of weighted visited learning objects i.e. vectors of visited learning objects or curriculum elements in which the student was interested (learner’s knowledge)
LEPi : ith Learner’s Educational Component
Learner’s educational preferences and Learning style.
Proposed Learner Model
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Learner’s Knowledge Component
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Learner’s Knowledge Component
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LO1 LO2 LO3
LO4
… LOn
LKi =
Learner’s Knowledge Component The learner’s knowledge component LKi can be represented as a matrix M(p, n), where p is the total number of learner’s sessions and n the cardinality of unique visited learning objects.
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Learner’s Educational Component Composed of the learner’s preferences among visited learning objects and his/her learning style.
Detection of the learner’s preferences: What kind of learning object does a learner prefer?
Learning objects available in LMS are characterized by many attributes (e.g. format), each of which may have several values (e.g. for format: text, image, video, etc.) that could be preferred or not by the learner. The preferences of a learner i upon these values can be represented, as a vector of interest measures :
Attributes Related values
Type_LO(Learning object type) {Resource, Activity}
Shape_LO(Learning object
shape, if Type_LO = Activity)
{Exercise, Simulation, Questionnaire, Assessment, Forum, Chat, Wiki, Assignment}
Format_LO(Learning object format, if Format_LO = Resource)
{Text, HTML, Image, Sound, Video}
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Learner’s Educational Component
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Attributes and corresponding values
Interest measures
Type_LO Resource LOIM_Type_LOiResource
Activity LOIM_Type_LOiActivity
Shape_LO Exercice LOIM_Shape_LOiExercice
Questionnaire LOIM_Shape_LOiQuestionnaire
test LOIM_Shape_LOiTest
Forum LOIM_Shape_LOiForum
Chat LOIM_Shape_LOiChat
Wiki LOIM_Shape_LOiWiki
Navigation LOIM_Shape_LOiNavigation
example LOIM_Shape_LOiExeamle
Format_LO Text LOIM_Format_LOiText
Html LOIM_Format_LOiHtml
Image LOIM_Format_LOiImage
Audio LOIM_Format_LOiAudio
Vidéo LOIM_Format_LOiVideo
Learner’s Educational Component
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Actifve/Reflective
Sensing/Intuitive
Visual/Verbal
Sequential/Global
F S L S M
Content_visit(-) Content_stay(-) Outline_stay(-) Selfass_stay(-)
…. ….
Active/Reflective Example_visit(+) Example_stay(+) Selfass_visit(+) Selfass_stay(+)
…. ….
Sensing/Intuitive Ques_text(-) Forum_visit(-) Forum_stay(-) Forum_post(-)
…. ….
Visual/Verbal Outline_stay(-) Ques_detail(+) Ques_overview(-) Ques_interpret(-)
…. ….
Sequential/Global
Commonly incorporated features in LMS
• Content • Outline • Example • Self-Assessment • Exercice • Forum • Navigation
Learner’s Educational Component Detection of the learning style (Graf et al., 2008)
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LPi , , {(LEP1, .. , LEPp), LSi}
Once learner models are built, we apply a hierarchical multi-
level model based collaborative filtering approach on these
models, in order to assign learners with common preferences and
interests to the same groups, so that feedback from one learner
can serve as a guideline for information delivery to the other
learners within the same group.
Group Modeling
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Learning Styles
Educational Preferences
…
Level1 : Classification
Level2 : Clustering
…
Group vectors
W1 W2 W3
.
.
. Wi ..
Wm
…
LS1 LS2 LS8
… …
Level3 : Clustering
… … … …
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The proposed approach has been implemented and an experiment was performed as part of a recommendation approach
Recommendations are computed with respect to the learner’s clickstreams, his/her learning style and educational preferences, as well as exploiting similarities and dissimilarities among the learner models and educational content.
Moodle LMS is used to implement the proposed approach. We used an online hybrid course (C2i) with 704 learners. Tracked data was successfully extracted from various Moodle tables (primarily from mdl_log table).
Implementation and Proof of Concept Evaluation
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Out
put
com
pone
nt 1
Implicit Query Extractor
Cp1.2 Terms’ Vector Builder Fenêtre glissante (K termes
pertinents)
Cp1.1 LO Vector Builder Fenêtre glissante (SW LO)
Cp1.3 LEP Extractor Extraction des préférences
pédagogiques
(Sessionizing)
com
pone
nt 2
Learners’ Models Builder
Cp2.1 LK Builder
Cp2.2 LEP Builder
Cp2.3 Groups Builder
(Indexing | Retrieving)
com
pone
nt 3
Content Model Builder
Cp3.1 LO converter
Cp3.2 Terms Extractor
Cp3.3 Indexer
Recommendation Engine
com
pone
nt 4
Cp4.1 Collaborative Recommender
Cp4.2 Content Recommender
Cp4.3 Hybridizer
Input
Learner (Active Session)
LO
com
pone
nt 5
H
AR
SYPE
L C
onfig
urat
ion
Mod
ule
HA
RS
YP
EL
Logs + DB Usage data
Learning Styles Models
HARSYPEL Architecture
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Conclusions and Future Work
Developed an approach for automatically modeling learners (groups) within LMS taking into account their educational preferences and learning styles
Proposed approach falls within the scope of building an educational automatic hybrid recommender system providing suitable recommendations to learners for personalized technology enhanced learning ;
Future work: Evaluation of the recommender system
ICALT 2013, Beijing, China 15/07/2013