Research Output

Adaptive learning in computing for non-native speakers.

  A significant proportion of e-Learning resources for engineering and computing education appear to be exclusively in English, requiring many learners to adapt themselves to learning within an English language context. Adaptive learning has a role to play in minimizing this adjustment and strengthening the learning. This research plans to understand learning needs, and take a Content and Language Integrated Learning (CLIL) approach to create algorithms to supply online learning experiences and content to meet these needs, adding novel mechanisms to help learners cope, develop their language capabilities, and enhance their ability to learn in another language. This work-in-progress describes the early stages of the research and we welcome insights into taxonomies of adaptive learning techniques, and mixed methods approaches to evaluating learning effectiveness, for those learning in an additional language.

  • Publication Status:

    Published

  • Publisher

    IEEE

  • DOI:

    10.1109/FIE.2014.7044142

  • Library of Congress:

    LB2300 Higher Education

  • Dewey Decimal Classification:

    378 Higher education

Citation

Rimbaud, Y., McEwan, T., Lawson, A. & Cairncross, S. (2013). Adaptive learning in computing for non-native speakers. In 2014 IEEE Frontiers in Education Conference Proceedings, 1-4. doi:10.1109/FIE.2014.7044142. ISBN 978-1-4799-3921-3

Authors

Keywords

Adaptive learning;

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