NSERC/iCORE/Xerox/Markin Industrial Research Chair programNSERC/iCORE/Xerox/Markin Industrial Research Chair program

The 2nd annual international conference on technology for

Education, T4E 2010

Presentation for short paper ‘A Practical Student Model for a Presentation for short paper ‘A Practical Student Model for a Location-Aware and Context-Sensitive Personalized Adaptive Location-Aware and Context-Sensitive Personalized Adaptive

Learning System’ Learning System’ by Guangbing Yang, Kinshuk, & Sabine Graf by Guangbing Yang, Kinshuk, & Sabine Graf

Athabasca University, CanadaAthabasca University, Canada

NSERC/iCORE/Xerox/Markin Industrial Research Chair programNSERC/iCORE/Xerox/Markin Industrial Research Chair program

A Practical Student Model for a Location-Aware A Practical Student Model for a Location-Aware and Context-Sensitive Personalized Adaptive and Context-Sensitive Personalized Adaptive

Learning SystemLearning System

• There is a demand for tailoring learning context (formal and informal) and content for each individual learner.

• There is a requirement of using knowledge embedded in the learning content from a personalized adaptive learning environment.

• According to [1], personalization is a key issue in the adaptive learning environments, and it relies on the student modeling.

[1] Devedzic, V. (2006). Semantic Web and Education, New York: Springer.

NSERC/iCORE/Xerox/Markin Industrial Research Chair programNSERC/iCORE/Xerox/Markin Industrial Research Chair program

Our ApproachOur Approach

• The student model presented here is a compound student model bringing together various features of the overlay, fault, and stereotype models to provide a generic way to modeling student with specific domain related knowledge in the context of a personalized adaptive learning system, namely Personalized Adaptive Learning Dashboard (PALD).

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Concept: 3-D Student ModelConcept: 3-D Student Model

Dimension map of the student model in PALD

The characteristics of a student are categorized The characteristics of a student are categorized into three dimensions, flexibly stable, into three dimensions, flexibly stable, dynamic, and domain-dependent.dynamic, and domain-dependent.

1.1. Flexibly stable dimensionFlexibly stable dimension includes student’s demographics, cognitive or learning styles, learning objectives and goals.

2.2. Dynamic dimensionDynamic dimension includes student’s experiences to the learning systems, skills, social collaborations, learning actions, learning behaviors, learning motivations, attitudes, and tutor’s feedbacks.

3.3. Domain-dependent dimensionDomain-dependent dimension includes student’s knowledge level in a specific domain topic, student’s interests to certain topics or subjects, and events raised during learning processing

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Practice: Student Modeling in Practice: Student Modeling in Personalized Personalized Adaptive Learning Dashboard (PALD)Adaptive Learning Dashboard (PALD)

• It receives reports of student’s learning activities from components that are either integrated with PALD or as external applications. The reported activities include contents the student read, questions answered, links followed, discussion forums joined, social networks joined or visited, and components visited.

• It then validates the model by estimating the knowledge about different topics from the learning materials the student supposes to learn. Then, it models these information and outcomes a student profile defined by following DTD file. (See next slide for details about the generated student profile and student-profile.dtd).

The process of student modeling in the PALD is as followsThe process of student modeling in the PALD is as follows

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Practice: Student Modeling in PALD –Practice: Student Modeling in PALD –Student-profile and dtdStudent-profile and dtd

NSERC/iCORE/Xerox/Markin Industrial Research Chair programNSERC/iCORE/Xerox/Markin Industrial Research Chair program

Practice: Student Modeling in PALD –Practice: Student Modeling in PALD –Student-profile and dtdStudent-profile and dtd

Demonstration – Student modeling in PALD Demonstration – Student modeling in PALD Instructor PortalInstructor Portal

• Student ProfileStudent Profile

Demonstration – Student modeling in Demonstration – Student modeling in PALD Instructor PortalPALD Instructor Portal

• Student Learning ActivitiesStudent Learning Activities

Demonstration – Student modeling in Demonstration – Student modeling in PALD Student PortalPALD Student Portal

• Context-awareness and multi-platforms and multi-devices support

The furtherThe further workwork

• Continue the development of the student model and design and develop a way to fine tune the parameters used in the student modeling.

• Involve the student group concept to facilitate the student model initialization processing.

• Consider the semantic based distribution services derived from the adaptive context and reuse of personalization functionalities against the requirements

of a mobile environment.

Thank You!Thank You!