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Further Thoughts on Context-Aware Paper Recommendations for Education

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Recommender Systems for Technology Enhanced Learning

Abstract

Simply matching learner interest with paper topic is far from enough in making personalized paper recommendations to learners in the educational domain. As such, we proposed the multidimensional recommendation techniques that consider (educational) context-aware information to inform and guide the system during the recommendation process. The contextual information includes both learner and paper features that can be extracted and learned during the pre- and post-recommendation process. User studies have been performed on both undergraduate (inexperienced learners) and graduate (experienced learners) students who have different information-seeking goals and educational backgrounds. Results from our extensive studies have been able to show that (1) it is both effective and desirable to implement the multidimensional recommendation techniques that are more complex than the traditional single-dimensional recommendation; (2) recommendation from across different learning groups (with different pedagogical features and learning goals) is less effective than that from within the same learning groups, especially when collaborative filtering technique is applied.

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Notes

  1. 1.

    For a more complete discussion on educational data mining, readers can refer to [20].

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Author information

Authors and Affiliations

  1. Department of Computer Science, Kean University, Union, NJ, USA

    Tiffany Y. Tang & Pinata Winoto

  2. Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada

    Gordon McCalla

Authors
  1. Tiffany Y. Tang
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  2. Pinata Winoto
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  3. Gordon McCalla
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Corresponding author

Correspondence to Tiffany Y. Tang .

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Editors and Affiliations

  1. Agro-Know, Athens, Greece

    Nikos Manouselis

  2. Faculty of Psychology and Educational Sciences, Welten Institute – Research Centre for Learning, Teaching and Technology, Open University of the Netherlands, Heerlen, The Netherlands

    Hendrik Drachsler

  3. Department of Computer Science, VUB & KU Leuven, Leuven, Belgium

    Katrien Verbert

  4. aDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, Madrid, Spain

    Olga C. Santos

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Tang, T.Y., Winoto, P., McCalla, G. (2014). Further Thoughts on Context-Aware Paper Recommendations for Education. In: Manouselis, N., Drachsler, H., Verbert, K., Santos, O. (eds) Recommender Systems for Technology Enhanced Learning. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0530-0_8

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  • DOI: https://doi.org/10.1007/978-1-4939-0530-0_8

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