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 kinshuk@ieee.org http://kinshuk.athabascau.ca

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

Technology Enhanced Knowledge Research Personalization & Adaptivity Social Networking Mobile Learning

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

Technology Enhanced Knowledge Research Mobile Learning Workplace English Modules http://wpeau.ca

Technology Enhanced Knowledge Research Social Networking Research on building effective learning communities The Landing (http://landing.athabascau.ca) 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)

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

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

~ Learning omnipresent and highly contextual ~ Vision ~ Learning omnipresent and highly contextual ~

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

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

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.

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

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

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

Mobile Adaptation Framework

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

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

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

Location Aware Learning and Content Creation - Mobile Virtual Campus (Lead: Dr Qing Tan) Demo http://www.youtube.com/watch?v=sPJRcnK50so (MVC part1.mp4) http://www.youtube.com/watch?v=WPgWgvnQwGI (MVC part2.mp4)

Mobile and ubiquitous educational environment Example scenario

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

Thank you!