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SEMANTIC WEB TECHNOLOGIES FOR PERSONALIZED LEARNING AND COLLABORATIVE TEACHING

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. Outline.

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SEMANTIC WEB TECHNOLOGIES FOR PERSONALIZED LEARNING AND COLLABORATIVE TEACHING

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  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. IEEE Learning Object Metadata: An Ontological Representation Emerging Technologies for Educational Resources Indexing & Re-use • Conlan, O., Hockemeyer, C., Lefrere, P., Wadde, V., Albert, A., 2001, Extending Educational Metadata Schemas to describe Adaptive Learning Resources, ACM ISBN 1-59113-420-7/01/0008 • Qin, J. & N. Hernandez. (2004). Ontological representation of learning objects: building interoperable vocabulary and structures. WWW2004, May 17-22, 2004, New York, 348-349. 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

  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

  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. Understand The Roles of PEOnto • 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

  18. Profile Structure and Its Related Information

  19. Ontology-driven Profile Construction

  20. 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

  21. Curriculum Ontology Curriculum Ontology

  22. 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

  23. Language Ontology (ESL)

  24. Language Ontology (ESL)

  25. Instances of Language Ontology

  26. English Learning Objective Hierarchy

  27. 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.

  28. PedaOnto Inner Ontologies Figure 6.20

  29. Pedagogy Ontology

  30. PedaOnto Overview

  31. The Instructional Conditions, Instructional Methods and Instructional Outcomes of the Instruction Ontology. Figure 6.29

  32. Marco and Micro Views Figure 6.30 Figure 6.31

  33. PEOnto Relations

  34. Objective Links between different Ontologies Figure 6.18

  35. Objectives Hierarchy Figure 6.17

  36. Objective Classes

  37. Verbs of Competencies

  38. Material Information

  39. 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

  40. PEAs Ontological Commitments

  41. 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

  42. 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

  43. 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

  44. Ontology-driven Architecture for PIP

  45. 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.

  46. Personalized Instruction Planner

  47. Personalized Instruction Planner

  48. Personalized Instruction Planner

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