1. Slide 1 The 5R Adaptation Framework for Location- Based Mobile Learning Systems Kinshuk, PhD Associate Dean, Faculty of Science & Technology Professor, School of Computing and Information Systems NSERC/iCORE/Xerox/Markin Industrial Research Chair for Adaptivity and Personalization in Informatics Athabasca University, Canada kinshuk@ieee.org http://kinshuk.athabascau.ca (jointly with Qing Tan, Xiaokun Zhang and Rory McGreal)

2. 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 Slide 2

3. Slide 3 Vision ~ Learning omnipresent and highly contextual ~ So how do we do it?

4. Slide 4 Adaptivity 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

5. Slide 5 Learner awareness Personalization of learning experience through the dynamic learner modeling Performance based model Cognitive trait model Learning styles

6. Slide 6 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

7. Slide 7 Technology awareness Personalization of learning experience through the identification of technological functionality Identifying various device functionality Dynamically optimize the content to suit the functionality Display capability, Audio and video capability, Multi- language capability, Memory, Bandwidth, Operation platform

8. Slide 8 Location awareness Personalization of learning experience through the use of location modeling Location based optimal grouping Location based adaptation of learning content Location based collaborative creation of authentic content

9. Slide 9 Location based technologies

10. Slide 10 Location aware dynamic grouping Location Grouping Mobile Learner’s Address Mobile Learner’s Cellular Data Mobile Learner’s GPS Coordinates Mobile Learner’s Other Location Info Mobile Virtual Campus Mobile Learner’s Learning Profile Mobile Learner’s Learning Style Mobile Learner’s Learning Interests

11. Slide 11 Location based content creation

12. Slide 12 Real-life physical objects Personalization of learning experience as per surrounding environment Public databases of POIs QR Codes Wi-Fi & Bluetooth Access Point identification Active and Passive RFIDs

13. Slide 13

14. Slide 14 Surrounding context Personalization of learning experience through the use of surrounding context Identifying specific context-aware knowledge structure among different domains Identify the learning objective(s) that the learner is really interested in Propose learning activities to the learner Lead the learner around the learning environment

15. Slide 15 5R Adaptation Framework

16. Slide 16 Introduction of framework A conceptual framework for the implementation of Adaptive Mobile Learning systems. An ontology model of the framework in which the factors of Learner, Location, Time, and Mobile Device are considered in generating Personalized Learning Contents

17. Slide 17 Presenting or generating personalized learning contents and instructions dynamically Learning environment and mobile device The context of learning process and instruction Appropriately identifying characters of particular learner. Dynamic Contents Mobile Device Context Aware Learner Identification  Challenge of facilitating mobile learning and ensuring learners’ performance: Introduction of framework (cont. 1)

18. Slide 18 Introduction of framework (cont. 2) The challenge facing the development of location- based adaptive learning applications is the ability to deal with these contexts from Learning Perspective. One of the key strategies is to: identify and normalize context information based on efficient context-aware data fusion. semantic-based context constraints using composable ontology models.

19. Slide 19 Introduction of framework (cont. 3) The Ontology-based approach: Uses predefined metadata models of the learning contents, learner models, context information of the learning activities, and mobile device, etc. Retrieve structured and unstructured learning materials and generate personalized, just-in-time, and location-aware learning contents or adaptive “filter” that directs mobile learner to access right contents.

20. Slide 20 Introduction of framework (cont. 4) The First approach: To create semantic learning contents manually. The Second approach: To take advantage of pre-existing learning objects. To develop shareable ontologies, publishing learning objects standard, and reward mobile service system to make the learning objects widely accessible. Our Research Aim: To conduct bottom-up development of the ontology for the personalized learning objectives, learning context information and proposed 5R constraint information.

21. Slide 21 Introduction of framework (cont. 5) The Third approach: To develop software and knowledge retrieval mechanism that automatically identifies appropriate learning components and extracts structural knowledge from unstructured learning contents. Learning contents are pre-developed and stored in the learning contents repository of the learning management system. Our Research Aim: To build and manipulate adaptive “filter” to direct just-in-time retrieval paths during the mobile learning processes.

22. 5R Adaptation Framework Slide 22

23. The Right Time: Factors: the Date-Time and the Learning Progress Alberta Legislature: Open: 09:00 AM Close: 04:30 PM Device Date Time: 03:25 PM Timing Match! Show Contents! 5R Adaptation Framework

24. The Right Location: The learner’s current geographic location GPS Coordination Match! Show Contents! 5R Adaptation Framework

25. The Right Device:  HD Video Contents  Audio Contents  Text Contents  Web Page Contents  Flash Video Contents 5R Adaptation Framework

26. LO A1 LO P2 LO H3 Take Picture Take Picture Screen Shot Screen Shot Back Home Back Home Design Manner of Legislature Building LO A1 Take Picture Take Picture Screen Shot Screen Shot Back Home Back Home 5R Adaptation Framework The Right Contents: Learning objects, learning activities, and leaning instruction

27. The Right Learner:  Physical Education  Practical English  Art  Computer Science  Mathematics 5R Adaptation Framework

28. Slide 28 Implementation of framework The First layer: Consists of “Location”, “Time”, “Learner”, “Device”, and “Learning Contents”, respectively representing the five adaptation inputs. The Second layer: Further description of information or data of each adaptation input. The ontology scheme, namely, the relationships among the adaptation inputs, which illustrates why the inputs need to be described and how the inputs are interconnected.

29. Slide 29 Framework ontology schema

30. Slide 30 Framework application scenario: Concept

31. Slide 31 Framework application scenario: Field trip Location-based mobile fieldtrip applications at a zoo

32. Slide 32 Framework application scenario: Field trip Location-based mobile fieldtrip applications using visualized interaction with dynamic geospatial data

33. Slide 33 5R adaptation features in the fieldtrip scenario Location-based experiment Lab interface and visualized interaction on mobile devices. Adaptive learning content retrieval constrained by the location and ongoing fieldtrip activities. Visualized fieldtrip plan, real-time activity collaboration and monitoring during the fieltrip Fieldtrip Scenario Dynamic annotation or blog on the visualized semantic physical object model. Real-time sharing experience between students and others who are in the field or in remote areas via visualized virtual interaction interface.

34. Slide 34 Framework application scenario: RFID Classroom System Screenshot: User Login  System Screenshot: Learning Object - Round Exicter @ Location code 80

35. Slide 35 Framework application scenario: RFID Classroom  System Screenshot: Learning Object - Rectangular Exicter @ Location code 3F

36. Slide 36 Mobile and ubiquitous educational environment

37. Slide 37

38. Slide 38 Thank you!