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Quality Rating and Recommendation of Learning Objects

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E-Learning Networked Environments and Architectures

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Abstract

The unceasing growth of the Internet has led to new modes of learning in which learners routinely interact on-line with instructors, other students, and digital resources. Much recent research has focused on building infrastructure for these activities, especially to facilitate searching, filtering, and recommending on-line resources known as learning objects. Although newly defined standards for learning object metadata are expected to greatly improve searching and filtering capabilities, learners, instructors, and instructional developers may still be faced with choosing from many pages of object listings returned from a single learning object query. The listed objects tend to vary widely in quality. With current metadata and search methods, those who search for learning objects waste time and effort groping through overwhelming masses of information, often finding only poorly designed and developed instructional materials. Hence, there is a clear need for quality evaluations prior to making a recommendation that can be communicated in a coherent, standardized format to measure the quality of learning objects.

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Author information

Authors and Affiliations

  1. SIAT Simon Fraser University-Surrey Campus, 13450-102 Ave, Suite 1400, Surrey, BC, V3T5X3, Canada

    Vivekanandan Kumar

  2. Faculty of Education, Simon Fraser University—Burnaby, Campus 8888 University Drive, Burnaby, BC, V5A1S6, Canada

    John Nesbit & Philip Winne

  3. Educational Psychology & Leadership Studies, University of Victoria, PO Box 3010, STN CSC, Victoria, BC, V8W 3N4, Canada

    Allyson Hadwin

  4. Center for Online and Distance Education, Simon Fraser University, 1300 West Mall Complex 8888 University Drive, Burnaby, BC, V5A 1S6, Canada

    Dianne Jamieson-Noel

  5. SIAT Simon Fraser University-Surrey, Campus 13450-102 Ave, Surrey, BC, V3T5X3, Canada

    Kate Han

Authors
  1. Vivekanandan Kumar
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  2. John Nesbit
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  3. Philip Winne
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  4. Allyson Hadwin
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  5. Dianne Jamieson-Noel
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  6. Kate Han
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Ecole Polytechnique de Montreal, Canada

    Samuel Pierre PhD

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© 2007 Springer-Verlag London Limited

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Kumar, V., Nesbit, J., Winne, P., Hadwin, A., Jamieson-Noel, D., Han, K. (2007). Quality Rating and Recommendation of Learning Objects. In: Pierre, S. (eds) E-Learning Networked Environments and Architectures. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84628-758-9_12

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  • DOI: https://doi.org/10.1007/978-1-84628-758-9_12

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-351-2

  • Online ISBN: 978-1-84628-758-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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