Campus Sorocaba

Learning Objects Retrieval from Learning Objects Retrieval from Contextual Analysis of User Preferences Contextual Analysis of User Preferences to Enhance E-learning Personalizationto Enhance E-learning Personalization

LERIS-Laboratory of Studies in Networks, Innovation and Software

www.leris.sor. ufscar.br

Federal University of São Carlos - Sorocaba, Brazil

Luciana A M Zaina and Graça Bressan

Available in:

• Draft: http://www.dcomp.sor.ufscar.br/lzaina/papers/ICWI2009_draft.pdf• Final version: http://connection.ebscohost.com/c/articles/63798599/learning-objects-retrieval-from-contextual-analysis-user-preferences-enhance-e-learning-personalization

IntroductionIntroduction The personalization of a learning process occurs

through the investigation of the student’s preferences by tracking his interaction with the environment.

Adherence to the user’s preferences and to the content exhibited to the student may be enhanced by correlating learning objects and learning styles.

The observation of learning styles is one of the techniques that provide users with different teaching strategies, meeting the student’s individual needs.

Paper ObjectivePaper Objective To present a mechanism to retrieve learning objects

based on the analysis of user preference data from contextual information about student interactions.

This mechanism is performed by a component of a system architecture and it is based on the student’s classification in a specific learning profile. Felder and Silverman Model is adopted to classify

the student learning profile. A relationship between the categories of preferences

and the learning objects is used to build automatically the learning scenarios according to the student learning profile.

E-Learning Personalization IssuesE-Learning Personalization Issues

Important issues to support personalization: Learning objects User preferences Context-aware applications

Learning ObjectLearning Object It can be defined as an entity to be applied in a teaching-

learning process. e-learning: the aim is to create contents in digital formats.

Metadata usually is adopted to organize learning objects, improving their reuse.

The LOM (Learning Object Metadata) standard of the Institute of Electrical and Electronics Engineers – IEEE is the metadata specification used in the area of learning objects. It has a structure that describes learning objects through

descriptor categories.

Examples of LOM CategoriesExamples of LOM CategoriesLOM Category LOM Field Characterization

Technical Media Format (video type, sound)

Technical features description.

SizePhysical location Requirements (object use: software version, for example)

Educational Interactive type (active, expositive) Educational function

and pedagogicalcharacteristics object description.

Learning Resource Type (exercise, simulation, questionnaire)

User PreferencesUser Preferences The user preferences may be observed through his learning

style. The learning style involves the strategies that a student

tends to apply frequently to a given teaching situation. The Felder-Silverman Learning Style Model is describe by

dimensions of Learning and Teaching Styles, creating a relationship to learning styles and teaching strategies that could be adopted to support the student learning style.

The Felder-Silverman model was selected to this work, because it's close relationship to learning styles and teaching strategies, resulting in an adherence between these aspects.

Dimensions of Felder-Silverman Dimensions of Felder-Silverman Learning Style ModelLearning Style Model

Learning Style Teaching Strategies

Features

sensory concrete It is related with the perception of content.intuitive abstract

visual visual It is related with the format of content presentation.auditory verbal

active active It is related with the student participation in the activities.reflective passive

sequential sequential It is related with the best order to present the content: step-by-step progression or a overview first of content.

global global

Context-aware applicationsContext-aware applications

They were developed in the field of ubiquitous computation.

Context is used to characterize a given interactive situation. A set of relevant conditions and influences in the

interaction. It may support the dynamic composition of an

application offering suitable services and information to the user.

E-Learning E-Learning ArchitectureArchitecture

This paper proposal is based on the presented architecture:

LearnPESLearnPES

Learning Profile Evaluation System. It is responsible for modeling the learning profile, providing the

Monitoring API with the features used during the observation of the student’s interaction.

It suggests the learning profile based on contextual information and learning style models previously defined by the teacher.

LearnPES

Context Information

Learning Profile Models

Student Model

Monitoring API

Observable features

Suggested learning profile

Step I - Step I - LearnPESLearnPES

The teacher will determine the relevant observable features.

One observable feature will reflect the student preference about the feature. Because of this the teacher must classify, during the observation planning, each observable feature in one of categories of preferences: Perception, Presentation Format, Presentation Order or Participation. These categories are adherent to dimensions of Felder-Silverman Learning Style Model.

The group of observable features will compose an Observation Model. The Observation Model will send to Monitoring Module to be used by tracking the student interaction in the e-learning environment.

Step II - Step II - LearnPESLearnPES

The next step is the values specification for each observed feature determining the learning profile types that will be adopted during classification process.

The values permit the system to distinguish the different types of learning profile considering the variety of observable features.

The teacher may specify the characteristics of each type of learning profile for the categories of preference used in the observed feature definition.

Step III - LearnPES Step III - LearnPES When a student completes a teaching module, the

monitoring module triggers an event to LearnPES, notifying it of the conclusion of the process and informing it who is involved in the interaction.

Based on this information, the LearnPES consolidates the contextual information about the student’s interaction.

The result of this consolidation will determine the values of each item described in the observable feature for a specific student, thus providing information to determine the student profile.

Then LearnPES suggests the learning profile, categorizing the user preferences.

Step IV - LearnPESStep IV - LearnPES After classifying the student according to a

learning profile, the LearnPES triggers an event to the LearnSBuilder to start the retrieval process.

LearnSBuilderLearnSBuilder It uses the categories of preferences to retrieve

the learning objects. It makes a correlation between the categories of

preferences (present in the student model) and the fields of LOM.

Learning Objects

Student Model

LearnSBuilder

Component to retrieve LOComponent to retrieve LO

The component carries out searches in repositories containing objects catalogued according to the LOM standard.

The maintenance of learning object repositories must be supported by the e-learning infrastructure that adopts the proposed architecture.

Learning Objects retrieval processLearning Objects retrieval process

Steps to localize learning objects

LOM fields LOM Category

Title, Description, and Keywords

Location ofconcepts

General

Finding the objects that match the

student’s learning profile

Interactivity and Learning Resource

Educational

LOLearning Objects selected

LO

LO

LO

LO

Steps to localize learning objects

LOM fields LOM Category

Title, Description, and Keywords

Location ofconcepts

General

Finding the objects that match the

student’s learning profile

Interactivity and Learning Resource

Educational

LOLearning Objects selected

LO

LO

LO

LO

Step I - Location of conceptsStep I - Location of concepts To this end, the search component looks for the subject

into the “General” category of the LOM specification by means of the fields: Title, Description, and Keywords.

The result is a LO set related to the subject.

It uses the “Educational” category (Interactivity and Learning Resource fields).

It identifies the objects that match the preferences related to the student’s learning profile in the set of objects obtained in the first locating step.

Step II – Location based on learning Step II – Location based on learning profile profile

LOM fields X Preference CategoriesLOM fields X Preference Categories

LOM Field Field ValuesProfile Feature

Preference Category

InteractivityActive Concrete

PerceptionExpositive Abstract

Learning Resource

Figure, Video, Film, and others

VisualPresentation-

FormatText, Sound, and others Auditory

Practical Exercise, Experiment, and others

Active

ParticipationQuestionnaire, and

ReadingsReflexive

Evaluation of the proposed mechanismEvaluation of the proposed mechanism

The mechanism was evaluated in an experiment applied in a group of Computer Engineering students during a Data Structure course.

The purpose of the experiment is to motivate the learners to complement their studies in a virtual environment.

During four months, the students will have access to extra material composed of videos, simulations, conceptual texts, case studies, objective tests, sounds, etc.

Conclusions and future works Conclusions and future works The development of flexible educational environments

that are adaptable has become an important requisite within the teaching-learning process.

The association between learning profiles and learning objects metadata grants dynamism in the content retrieval process.

Future directions: One important subject for future work is to extend

the architecture to considering to the retrieval mechanism the features of mobile learning as differences between devices.

Campus Sorocaba

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lzaina@ufscar.brlzaina@ufscar.br http://www.dcomp.sor.ufscar.br/lzaina http://www.dcomp.sor.ufscar.br/lzaina