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The Case for Preference-Inconsistent Recommendations

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Recommender Systems for Technology Enhanced Learning

Abstract

Critical thinking requires knowledge about the diversity of viewpoints on controversial issues. However, the diversity of perspectives often remains unexploited: Learners prefer preference-consistent over preference-inconsistent information, a phenomenon called confirmation bias. This chapter attempts to introduce how recommender systems can be used to stimulate unbiased information selection, elaboration and unbiased evaluation. The principle of preference-inconsistency and its role in supporting critical thinking is explained. We present our empirical approach, the experimental paradigm and a summary of our main findings. Taken together, the results indicate that preference-inconsistent recommendations are an effective approach for stimulating unbiased information selection, elaboration and evaluation. In conclusion, implications for research and practice are discussed.

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Authors and Affiliations

  1. Knowledge Media Research Center, Tübingen, Germany

    Christina Schwind & Jürgen Buder

Authors
  1. Christina Schwind
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  2. Jürgen Buder
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Corresponding author

Correspondence to Christina Schwind .

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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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Schwind, C., Buder, J. (2014). The Case for Preference-Inconsistent Recommendations. 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_7

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  • DOI: https://doi.org/10.1007/978-1-4939-0530-0_7

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-0529-4

  • Online ISBN: 978-1-4939-0530-0

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