1. SEMANTIC WEB TECHNOLOGIES FOR PERSONALIZED LEARNING AND COLLABORATIVE TEACHING
Apple W P Fok
Centre for Innovative Applications of Internet and Multimedia Technologies (AIMtech)
Image Computing Group, Department of Computer Science
City University of Hong Kong

2. Outline Goal & Motivation: Personalized Education (PE)
Conceptual Framework of Personalized Education System (PES)
PES Realization: Personalized Agents Team (PEAs)
Personalized Education Ontology (PEOnto): An Integration of multiple ontologies for PES
Application of PEOnto: Personalized Instruction Planner (PIP)
WELNET: A Collaborative Blended Learning Community for Personalized Learning and Collaborative Teaching

3. Education Reform and IT in Education

4. Personalized Education
Personalization in e-commerce:
capture & retain customers’ loyalty
Building a meaningful one-to-one relationship. – Riecken D.
Delivering appropriate content and services to fulfill user’s needs. – Monica Bonett
Understanding where and when to recommend the “right” things. – Oracle
Personalization
- Cater to individual learning differences (ability & needs)
- Machine learning and updating of student profiles
- Intelligent educational content search & filtering
- Automatic individualized study plan generation PES framework
[Fok & Ip 2004]

5. Personalization in Education
A supportive learning platform should:
Monitor and manage individual student profile
Provide a common structure for educational content annotation & indexing
Search and recommend materials relevant to individual learning needs
Intelligently sequence learning materials to meet individual learning objectives
Support education research through collecting and analyzing usage data of students and teachers (e.g. data-mining)
Adapt to student’s needs through analysis of learning progress (eg. adaptive educational hypermedia)

6. Personalization in Education
[Fok & Ip 2004]

7. The Framework of the PES
Fok & Ip, 2004

8. Architecture of PES Built upon Tsinghua University “Smart Platform”
Asynchronous communication
Support Publish-and-subscribe model
Loosely-coupled
Parallel Execution
Fok & Ip, 2005

9. Run time structure of PES
Dual-citizenship web server!

10. PE Agents’ Design
Fok & Ip, 2005

11. Emerging Technologies for Educational Resources Indexing & Re-use
IEEE Learning Object Metadata: An Ontological Representation
Conlan, O., Hockemeyer, C., Lefrere, P., Wadde, V., Albert, A., 2001, Extending Educational Metadata Schemas to describe Adaptive Learning Resources, ACM ISBN /01/0008
Qin, J. & N. Hernandez. (2004). Ontological representation of learning objects: building interoperable vocabulary and structures. WWW2004, May 17-22, 2004, New York, New York: ACM Press.
Recker, M.M., Wiley, D.A., 2000, A non-authoritative educational metadata ontology for filtering and recommending learning objects
Scime, A., and Kerschberg, L., 2000, WebSifter: An Ontology-based Personalizable Search Agent for the Web, International Conference on Digital Libraries: Research and Practice, Kyoto Japan, 2000
Kerschberg, L., Kim, W., and Scime, A., 2000, WebSifter II: A Personalizable Meta-Search Agent based on Semantic Weighted Taxonomy Tree 11

12. Educational Ontology Semantic Web
Technologies for describing content that are readable and can be processed by machine (eg. software search agent)
Extending Semantic Web to the Educational community:
Emerging standards for defining learning contents:
describing “structure” of learning objects [LOM]
describing “packaging, sequencing and presenting” reusable learning objects [SCORM]
Mechanism to relate different educational concepts to facilitate search of learning objects [Educational Ontology, OWL]

13. Semantic Metadata
Erik Duval
Dept. Computerwetenschappen
K.U.Leuven
Erik Duval, Metadata and Semantic Web, LORnet Conference, 18 November 2004, Montreal, Canada

14. Personalized Education Ontology (PEOnto)
Fok & Ip, 2006
An Educational Ontology
A fundamental component of PE
The development of a semantic web for educational resources
Facilitate personal epistemology in discovering, selecting, organizing and using relevant educational resources.
Incorporate FIVE interrelated educational ontologies
People Ontology
Language Ontology
Curriculum Ontology
Pedagogy Ontology
PEA Ontology

15. The Roles of PEOnto Strengthen agents communication and performances
Understand
Strengthen agents communication and performances
Ontological commitments
Automatic messages/parameters generations
Understand LO in a semantic way
Relevant for a particular task/activity
Fulfill a particular learning objective type
Sequence in relation to different LOs
Understand and Discover implicit information for further analyze
The relations between the instructional design (LO) and students’ learning
Different learning paths for different students’ learning needs (i.e. Cognitive, Skills or Affective Domain development)
Different teaching/learning styles and learning patterns

16. PEOnto Components
Fok & Ip, ICCE 2005

17. PEOnto – cont. People Ontology (PeOnto)
The structure of school education, people, schools and the activities perform between them
Construct the User Profiles based on the IMS Learner Information Package Specification and further extended the taxonomy for in-depth classification and mining purposes

19. Profile Structure and Its Related Information

20. Ontology-driven Profile Construction20

21. PEOnto – cont. Curriculum Ontology (CurOnto)
The structure of a curriculum design and its essential components and attributes
Represents the goal state of a user, a searching query, or classification of learning resources

22. Curriculum Ontology
Curriculum Ontology

23. PEOnto – cont. Language Ontology (LangOnto)
The structure of a subject domain
Classify educational resources into different language learning items
Discover the relations between knowledge, skills and levels

24. Language Ontology (ESL)

25. Language Ontology (ESL)

26. Instances of Language Ontology
Figure 6.12 shows an extracted portion of the Language Ontology descriptions that are being used for the classification.

27. English Learning Objective Hierarchy

28. PEOnto – cont. Pedagogy Ontology (PedaOnto)
Describes the pedagogical approaches, instructional design procedures and the relations between educational resources and instructional events/activities.
Pedagogy Ontology
Instruction Ontology
Content Ontology
Helps to identify the usability of various resources and discover teaching/learning preferences/styles.

29. PedaOnto Inner Ontologies
Figure 6.20

30. Pedagogy Ontology

31. PedaOnto Overview

32. The Instructional Conditions, Instructional Methods and Instructional Outcomes of the Instruction Ontology.
p. 180
Figure 6.29

33. Marco and Micro Views
Figure 6.30
Figure 6.31

34. PEOnto Relations

35. Objective Links between different Ontologies
Figure 6.18

36. Objectives Hierarchy
Figure 6.17

37. Objective Classes

38. Verbs of Competencies
Table 6.4 P.184

39. Material Information

40. PEOnto – cont. PE Agents Ontology (PEAOnto)
Governs PEAs behaviors/duties
Describes the responsibilities of each PE agent and indicates the relations and communication path among the PEA team

42. PEAs Ontological Commitments

43. Application of PEOnto Producing digitalized educational resources
Incorporating learning resources with appropriate pedagogies
Modifying, reusing, or improving existing educational resources effectively
Storing, retrieving and sharing educational resources as well as teaching experiences efficiently

44. Personalized Instruction Planner (PIP)
Fok and Ip, ICME 2006
Personalized Instruction Planner
Searching Tool
Selecting Tool
Organizing Tool
Personalized Education Agents (PEAs)
Crawling Agent
Classification Agent
Searching Agent
Personalized Education Ontology (PEOnto)
Curriculum Ontology
Pedagogy
Ontology
People
Ontology
Ontology Schema Databases
Personal/Content Profiles
PIP Learning Objects
PEOnto Schema and Metadata

45. Key Tasks of PIP Personalization Search
Retrieve personalized search results in respect to the user profiles
Personalized Instruction Planning
Organize and structure instruction plan according to school-based curriculum or teaching preferences
Record all instruction designs and identify various uses of education resources.
Generating PE LOM resources
Incorporate educational vocabulary items (i.e. PEOnto) to label and annotate PE resources as LOM for improved interoperability and reusability

46. Ontology-driven Architecture for PIP

47. Steps of Materials Selections
Objective Statements;
Objective Classification;
Selection of instructional events;
Determining type of stimuli for each event;
Listing the candidate resources for each event;
Listing the theoretically best resources for the events;
Recording final resources choices;
Generating a rationale for the decisions made and
Generating a prescription for each material in each event.

48. Personalized Instruction Planner

49. Personalized Instruction Planner

50. Personalized Instruction Planner

51. Personalized Instruction Planner
The HK English School Curriculum in PIP

52. Personalized Instruction Planner

53. Personalized Instruction Planner

54. Personalized Instruction Planner

55. Instruction Plan Design

56. Personalized Instruction Planner

57. PIP – Global Search

58. PE Search Workflow
Retrieve relevant educational resources from the Web
Internet
Web-crawling Agent
Classification Agent
Personalized Search Agent
Databases
Education Ontology
(PEOnto) 1 3 2
Filter and classify retrieved resources with respect to education goals, learning objectives, and instruction design principles
Response queries and collect feedbacks (i.e. usage results)

59. PIP – Global Search

60. PIP – Global Search

61. PIP – Local Search

62. PIP – Local Search

63. Customized Search
E.g. The message path of customized search request and response

64. PIP – Local Search

65. Personalized Instruction Planner65

66. Personalized Instruction Planner66

67. PES Performance Simulation
Stub Implementation
Run the service of planning, searching & filtering simultaneously
Assume each service per time costs 10 ms

68. The Past A Conceptual framework for Personalized Education
The design and development of the PES
Central to the PE Framework is PEOnto - an integration of FIVE inter-related ontologies
PEOnto demonstrates the necessary attributes required in Personalized Education services delivery
Applied PEOnto in the development of PIP for English Second Language (ESL) Learning
PIP provides a testbed not only for evaluating the feasibility of PES, but more importantly, experiencing different mechanisms and strategies to realize our vision in education – Personalized Education

69. Present
Authoring and Delivering Sharable, Reusable, Pedagogically Sound Education Resources
Further exploit PIP potentials in WELS
Better response time, higher automation, multiple subjects, Chinese encoding, better interface designs and so on…
Further adjust to fulfill more instructional design needs
Try out different approaches and develop/explore new E-pedagogy approaches/models

70. Future Work The Personalized Education System and its PEAs
A user-friendly interface for teachers to annotate and deliver educational resources
Personalized Education Features
More subject domain ontologies
A localized intelligent education search engine
Experience and compare different agent design and algorithms so as to provide personalized e-learning experience to support teaching & learning through PES
Profiling and Mining
Task Performance Support

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