Toward A Theoretical Basis for Dynamically Driven Content

Kathleen Scalise

ISBN 10: 3838310373 / ISBN 13: 9783838310374

Published by LAP LAMBERT Academic Publishing Mai 2010, 2010

About this Item

Neuware -The intent of dynamic learning with data driven content (DDC) in computer-mediated learning environments is to interactively adapt the flow of content so that each student receives personalised learning materials and interventions more suited to their needs than in traditional one-size-fits-all applications. Measurement technologies similar to some models underlying computer-adaptive testing approaches (CAT) are used here to create personalisation by mapping knowledge spaces and driving computer-mediated learning environments. Methods explore extensions to CAT with item response models and construct mapping, which may direct the flow and difficulty not only of assessments but also of other e- learning materials and feedback to tailor the learning experience to student needs. A measurement model, the iota model, is introduced and tested as a multifacet Rasch model to estimate 'pathway' parameters through BEAR CAT testlets. Testlets are small bundles of items that act as questions and follow-up probes to interactively measure and assign scores to students. The function of the measurement models applied is mathematically equivalent to the semi-linear neural net model.Books on Demand GmbH, Überseering 33, 22297 Hamburg 332 pp. Englisch.

Seller Inventory # 9783838310374

Bibliographic Details

• Title: Toward A Theoretical Basis for Dynamically Driven Content
• Publisher: LAP LAMBERT Academic Publishing Mai 2010
• Publication Date: 2010
• Language: English
• ISBN 10: 3838310373
• ISBN 13: 9783838310374
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

The intent of dynamic learning with data driven content (DDC) in computer-mediated learning environments is to interactively adapt the flow of content so that each student receives personalised learning materials and interventions more suited to their needs than in traditional one-size-fits-all applications. Measurement technologies similar to some models underlying computer-adaptive testing approaches (CAT) are used here to create personalisation by mapping knowledge spaces and driving computer-mediated learning environments. Methods explore extensions to CAT with item response models and construct mapping, which may direct the flow and difficulty not only of assessments but also of other e- learning materials and feedback to tailor the learning experience to student needs. A measurement model, the iota model, is introduced and tested as a multifacet Rasch model to estimate "pathway" parameters through BEAR CAT testlets. Testlets are small bundles of items that act as questions and follow-up probes to interactively measure and assign scores to students. The function of the measurement models applied is mathematically equivalent to the semi-linear neural net model.

About the Author

Kathleen Scalise received her Ph.D. in quantitative measurement at the University of California, Berkeley. An assistant professor at the University of Oregon, she served as a writer of California's K-12 Science Framework, as UC Berkeley Chancellor's speechwriter, and works on dynamically delivered content in eLearning with item response models.