Toward a fully automatic learner model based on web usage...

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

[email protected]

LaTICE, University of Tunis ICALT 2013, Beijing, China.

Mohamed JEMNI University of Tunis

[email protected]

Olfa NASRAOUI

University of Louisville [email protected]

Sabine GRAF Athabasca University

[email protected]

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


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


Learning Styles

Educational Preferences

…

Level1 : Classification

Level2 : Clustering

…

Group vectors

W1 W2 W3

.

.

. Wi ..

Wm

…

LS1 LS2 LS8

… …

Level3 : Clustering

… … … …

15 ICALT 2013, Beijing, China 15/07/2013

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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


Toward a fully automatic learner model based on web usage...

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