2. NSERC/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 Location-Aware and Context-Sensitive Personalized Adaptive Learning System’ by Guangbing Yang, Kinshuk, & Sabine Graf Athabasca University, Canada

3. NSERC/iCORE/Xerox/Markin Industrial Research Chair program A Practical Student Model for a Location-Aware and Context-Sensitive Personalized Adaptive Learning 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.

4. NSERC/iCORE/Xerox/Markin Industrial Research Chair program Our 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).

5. NSERC/iCORE/Xerox/Markin Industrial Research Chair program Concept: 3-D Student Model Dimension map of the student model in PALD The characteristics of a student are categorized into three dimensions, flexibly stable, dynamic, and domain-dependent. 1.Flexibly stable dimension 1.Flexibly stable dimension includes student’s demographics, cognitive or learning styles, learning objectives and goals. 2.Dynamic dimension 2.Dynamic dimension includes student’s experiences to the learning systems, skills, social collaborations, learning actions, learning behaviors, learning motivations, attitudes, and tutor’s feedbacks. 3.Domain-dependent dimension 3.Domain-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

6. NSERC/iCORE/Xerox/Markin Industrial Research Chair program Practice: Student Modeling in Personalized 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 follows

7. NSERC/iCORE/Xerox/Markin Industrial Research Chair program Practice: Student Modeling in PALD – Student-profile and dtd

8. NSERC/iCORE/Xerox/Markin Industrial Research Chair program Practice: Student Modeling in PALD – Student-profile and dtd

9. Demonstration – Student modeling in PALD Instructor Portal Student ProfileStudent Profile

10. Demonstration – Student modeling in PALD Instructor Portal Student Learning ActivitiesStudent Learning Activities

11. Demonstration – Student modeling in PALD Student Portal Context-awareness and multi-platforms and multi-devices support

12. The furtherwork The further work 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.

13. Thank You!