1. Knowledge management in transition from e-learning to ubiquitous learning: innovations and personalization issues
Research team:
Maiga Chang
Jon Dron
Sabine Graf
Vive Kumar
Oscar Lin
Qing Tan
Dunwei Wen
Guangbing Yang
Qingsheng Zhang
Kinshuk
NSERC/iCORE/Xerox/Markin Industrial Research Chair for Adaptivity and Personalization in Informatics
Athabasca University, Canada

2. Athabasca University Canada’s Open University
38,000 students in over 750 courses in about 90 programs (about 3600 grad students)
Research in e-learning a strategic priority

3. Technology Enhanced Knowledge Research
Personalization
& Adaptivity
Social Networking
Mobile Learning

4. Technology Enhanced Knowledge Research
Mobile Learning
Research on wide variety of wireless devices and applications
Widening access to education to remote and isolated communities
Aims to remove communication and educational barriers and makes learning convenient and available

5. Technology Enhanced Knowledge Research
Mobile Learning
Workplace English Modules

6. Technology Enhanced Knowledge Research
Social Networking
Research on building effective learning communities
The Landing (
Harnessing the power of “Web 2.0” and “Web 3.0”
Bridging the gap between formal and informal knowledge spaces
Research on immersive environments
(Sun Project Wonderland)

7. Technology Enhanced Knowledge Research
Personalization & Adaptivity
Research on designing innovative paradigms, architectures and implementations in learning systems
Individualized and customized learning in a global environment
Learner, device, environment and context modelling
Merging real-life objects with virtual interaction for authentic learning

8. Overall Research Direction
Individualised learning in increasingly global educational environment
Bridging the gap among different types of learners
Support for:
Mobile and life-long learners
Just-in-time and on-demand learning
Context adaptation

9. ~ Learning omnipresent and highly contextual ~
Vision
~ Learning omnipresent and highly contextual ~

10. Personalization in ubiquitous learning
Extensive modelling of learner’s actions, interactions, “mood”, trends of preferences, skill & knowledge levels, implicit and explicit changes in skill & knowledge levels
Real-time monitoring of learner’s location, technology use, and change of situational aspects

11. Learner awareness
Personalization of learning experience through dynamic learner modeling
Performance based model
Cognitive trait model
Learning styles

12. Cognitive Traits
Working Memory Capacity: allows us to keep active a limited amount of info (7+/-2 items) for short time (Miller, 1956).
Inductive Reasoning Ability: is the ability to construct concepts from examples.
Information Processing Speed: determines how fast the learners acquire the information correctly.
Associative Learning Skill: is the skill to link new knowledge to existing knowledge.
Domain Experience: is the familiarity of the domain concepts and skills.
Domain Complexity: is the student perception regarding difficulty of the concepts in the domain.

13. Felder-Silverman Learning Style Model
Each learner has a preference on each of the dimensions
Dimensions:
Active – Reflective learning by doing – learning by thinking things through group work – work alone
Sensing – Intuitive concrete material – abstract material more practical – more innovative and creative patient / not patient with details standard procedures – challenges
Visual – Verbal
learning from pictures – learning from words
Sequential – Global learn in linear steps – learn in large leaps good in using partial knowledge – need „big picture“ serial – holistic

14. FSLSM – How to find out the learning style?
Index of Learning Style (Felder & Soloman, 1997)
44-item questionnaire (11 questions per dimension)
+11 +9 +7 +5 +3 +1 -1 -3 -5 -7 -9
-11
active
reflective
Strong
preference
Moderate
preference
Well balanced
Moderate
preference
Strong
preference
Track learners behavior to infer learning style
Using Bayesian networks to detect learning styles
Detecting learning styles in learning managment systems

15. Dynamic learner modeling
Mining of historical and real time data for real-time adaptivity
Learning activities
Learning style
Interests & knowledge
Problem solving activities
Learning object/activity usage
Social activities
Learner location
Location related activities

16. Mobile Adaptation Framework

17. Technology awareness
Personalization of learning experience through the identification of technological functionality
Identify technologies available to the learner and surrounding the learner
Dynamically optimize the content to suit the functionality of available technologies

18. Location and surrounding awareness
Personalization of learning experience as per surrounding environment
Location based adaptation of learning content and collaboration activities
Public databases of POIs QR Codes
WiFi and Bluetooth access point identification
Active and passive RFIDs
Identifying context-aware semantically linked knowledge structures among different domains

20. 5R Adaptive Model and Process
(lead: Dr Qing Tan)
(Right Learner, Right Location, Right Device, Right Time and Right Contents)
User Info
User
Model
Device
Model
Device Info
Sensors
Info
Generation 5R
Adaptive
Contents
Description 5R
Contents
Creation 5R
Contents
Location Info
Location
Model
Time
Model
Time Info
5R Adaptive Model

21. Location Aware Learning and Content Creation - Mobile Virtual Campus (Lead: Dr Qing Tan) Demo (MVC part1.mp4) (MVC part2.mp4)

22. Mobile and ubiquitous educational environment Example scenario

24. View towards future….. Communities we serve….
Partnerships we nurture….
Projects we choose….
The funding we seek….
The HQP we develop….
Our capacity to respond….
Sustainable Research

25. Thank you!