CAMA 2007 Visions of the Future for Contextualized Attention Metadata
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This document summarizes a presentation by Wayne Hodgins on contextualized attention metadata and personalized learning. Some key points include: developing personalized learning experiences for each individual by delivering the right content, at the right time, on the right device based on their context; moving beyond learning objects to a model where content and context can be manipulated and reused separately; and the need for research on tools to analyze patterns in content and context metadata to optimize discovery and assembly of personalized learning experiences.
![[email_address] www.autodesk.com/waynehodgins Strategic Futurist President & Co-Founder Learnativity.org Chair, IEEE Learning Technology Standards Committee Learning Object Metadata 0 Strategic Advisor](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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CAMA 2007 Visions of the Future for Contextualized Attention Metadata
- 1. CAMA 2007 Contextualized Attention Metadata Workshop on Conteixtualized Attention Metadata: personalized access to digital resources Westin Bayshore Hotel Vancouver, British Columbia June 23, 2007
- 2. [email_address] www.autodesk.com/waynehodgins Strategic Futurist President & Co-Founder Learnativity.org Chair, IEEE Learning Technology Standards Committee Learning Object Metadata 0 Strategic Advisor
- 8. The Snowflake Effect Unique is What We Seek! Wayne Hodgins
- 12. “ C -ing the Future” C ontent, C ompetencies C ontext LEARNING & PERFORMANCE CONTENT CONTEXT COMPETENCE
- 15. Universal Object Model? Principle Fact Process Overview Procedure Text Audio Summary Concept ENABLING Objective E nabling L earning O bject T erminal L earning O bject Animation Simulation illustration TERMINAL Objective Common Content Application Specific Profiles Repurposed with Permission: W.Hodgins ©1992 Learnativity “ Raw” Data & Media Elements Information Blocks CONTENT ASSETS 0 SkillObject
- 16. Universal Object Model? Principle Fact Process Overview Procedure Text Audio Summary Concept Principle Process Concept Procedure Fact Overview Summary ENABLING Objective Collections ( Courses , Stories,) Animation Simulation illustration TERMINAL Objective Theme Enabling Objective Terminal Objective Common Content Application Specific Profiles Repurposed with Permission: W.Hodgins ©1992 Learnativity “ Raw” Data & Media Elements Information Blocks CONTENT ASSETS 0 SkillObject E nabling L earning O bject T erminal L earning O bject
- 17. Universal Object Model? Principle Fact Process Overview Procedure Text Audio Summary Concept Principle Process Concept Procedure Fact Overview Summary Objective E nabling L earning O bject T erminal L earning O bject Collections ( Courses , Stories,) Animation Simulation illustration Objective Theme Enabling Objective Terminal Objective Common Content Application Specific Profiles Repurposed with Permission: W.Hodgins ©1992 Learnativity “ Raw” Data & Media Elements Information Blocks CONTENT ASSETS 0 SkillObject CONTEXT ReUSABILITY
- 22. Thank You! For Questions & Comments please contact: [email_address] See “Off Course – On Target” for slides, podcasts, blogs and much more: www.autodesk.com/waynehodgins Slides available also @ http://www.slideshare.net/WayneH/
Editor's Notes
- Part of the ACM IEEE Joint Conference on Digital Libraries, June 17-23, 2007 – Vancouver, British Columbia, Canada Workshop on Contextualized Attention Metadata: personalized access to digital resources Effective and efficient access to relevant digital resources is one of the key challenges in digital libraries. Contextualized attention metadata (CAM) capture the attention that a user spends on such resources in a specific context. CAM enables us to better support the user in dealing with the information flood. Using CAM, filters can be devised that present new information only in the relevant context, for example by prioritizing incoming email based on the attention previously given to the topics of the email. Furthermore, CAM data can extend and amend user profiles thus enhances personalization in existing systems. CAM streams are collected from all applications that a user may interact with, including digital libraries, office suites, web browsers, multimedia players, computer-mediated communication and authoring tools, etc. The workshop intends to bring together researchers and practitioners from relevant communities (digital libraries, information systems, personalization, information retrieval, database systems, data mining, user modeling, psychology and technology enhanced learning, etc.) to share their knowledge, results and expertise about their research on attention metadata. In general, the workshop aims to foster and improve collaboration between communities, e.g. by discussing relevant cross-disciplinary research approaches for attention metadata. In more detail, the workshop aims to discuss suitable algorithms, techniques, technologies, architectures and designs to merge and process attention metadata. And finally, the workshop aims to evaluate the current status and progress of work on attention metadata in general and contextualized attention metadata specifically.