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Explainable Recommendations in a Personalized Programming Practice System

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Book cover Artificial Intelligence in Education (AIED 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12748))

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Abstract

This paper contributes to the research on explainable educational recommendations by investigating explainable recommendations in the context of personalized practice system for introductory Java programming. We present the design of two types of explanations to justify recommendation of next learning activity to practice. The value of these explainable recommendations was assessed in a semester-long classroom study. The paper analyses the observed impact of explainable recommendations on various aspects of student behavior and performance.

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Author information

Authors and Affiliations

  1. University of Pittsburgh, Pittsburgh, PA, USA

    Jordan Barria-Pineda, Kamil Akhuseyinoglu & Peter Brusilovsky

  2. University of Novi Sad, Novi Sad, Serbia

    Stefan Želem-Ćelap, Aleksandra Klasnja Milicevic & Mirjana Ivanovic

Authors
  1. Jordan Barria-Pineda
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  2. Kamil Akhuseyinoglu
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  3. Stefan Želem-Ćelap
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  4. Peter Brusilovsky
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  5. Aleksandra Klasnja Milicevic
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  6. Mirjana Ivanovic
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Corresponding author

Correspondence to Jordan Barria-Pineda .

Editor information

Editors and Affiliations

  1. Technion – Israel Institute of Technology, Haifa, Israel

    Ido Roll

  2. Arizona State University, Tempe, AZ, USA

    Danielle McNamara

  3. Utrecht University, Utrecht, The Netherlands

    Sergey Sosnovsky

  4. London Knowledge Lab, London, UK

    Rose Luckin

  5. University of Leeds, Leeds, UK

    Vania Dimitrova

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Barria-Pineda, J., Akhuseyinoglu, K., Želem-Ćelap, S., Brusilovsky, P., Milicevic, A.K., Ivanovic, M. (2021). Explainable Recommendations in a Personalized Programming Practice System. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12748. Springer, Cham. https://doi.org/10.1007/978-3-030-78292-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-78292-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78291-7

  • Online ISBN: 978-3-030-78292-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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