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A Mobile Context-Aware Framework for Managing Learning Schedules: Data Analysis from an Interview Study

A Mobile Context-Aware Framework for Managing Learning Schedules: Data Analysis from an Interview Study

Jane Yin-Kim Yau, Mike Joy
Copyright: © 2009 |Volume: 1 |Issue: 4 |Pages: 27
ISSN: 1941-8647|EISSN: 1941-8655|ISSN: 1941-8647|EISBN13: 9781616921057|EISSN: 1941-8655|DOI: 10.4018/jmbl.2009090803
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MLA

Yau, Jane Yin-Kim, and Mike Joy. "A Mobile Context-Aware Framework for Managing Learning Schedules: Data Analysis from an Interview Study." IJMBL vol.1, no.4 2009: pp.29-55. http://doi.org/10.4018/jmbl.2009090803

APA

Yau, J. Y. & Joy, M. (2009). A Mobile Context-Aware Framework for Managing Learning Schedules: Data Analysis from an Interview Study. International Journal of Mobile and Blended Learning (IJMBL), 1(4), 29-55. http://doi.org/10.4018/jmbl.2009090803

Chicago

Yau, Jane Yin-Kim, and Mike Joy. "A Mobile Context-Aware Framework for Managing Learning Schedules: Data Analysis from an Interview Study," International Journal of Mobile and Blended Learning (IJMBL) 1, no.4: 29-55. http://doi.org/10.4018/jmbl.2009090803

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Abstract

Mobile learning applications can be categorized into four generations: non-adaptive, learning-preferences based adaptive, learning-contexts-based adaptive and learning-contexts-aware adaptive. The research on our learning schedule framework is motivated by some of the challenges within the context-aware mobile learning field. These include being able to create and enhance students’ learning opportunities in different locations by considering different learning contexts and using them as the basis for selecting appropriate learning materials. We have adopted a pedagogical approach for evaluating this framework, an exploratory interview study with potential users consisting of 37 university students. The observed interview feedback gives us insights into the use of a pedagogical m-learning suggestion framework deploying a learning schedule subject to the five proposed learning contexts. Our data analysis is described and interpreted leading to a personalized suggestion mechanism for each learner and each scenario and a proposed taxonomy for describing mobile learner preferences.

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