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Learning Content Recommender System for Instructors of Programming Courses

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Artificial Intelligence in Education (AIED 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10948))

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Abstract

In this paper, we present a course-adaptive recommender system that assists instructors of programming courses in selecting the most relevant learning materials. The recommender system deduces the envisioned structure of a specific course using program examples prepared by the course instructor and recommends learning content items adapting to instructor’s intentions. We also present a study that assessed the quality of recommendations using datasets collected from different courses.

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Author information

Authors and Affiliations

  1. School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA

    Hung Chau, Jordan Barria-Pineda & Peter Brusilovsky

Authors
  1. Hung Chau
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  2. Jordan Barria-Pineda
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  3. Peter Brusilovsky
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Corresponding author

Correspondence to Hung Chau .

Editor information

Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Carolyn Penstein Rosé

  2. University of Technology, Sydney, NSW, Australia

    Roberto Martínez-Maldonado

  3. University of Duisburg-Essen , Duisburg, Germany

    H. Ulrich Hoppe

  4. UCL Institute of Education, London, UK

    Rose Luckin

  5. UCL Institute of Education, London, UK

    Manolis Mavrikis

  6. UCL Institute of Education, London, UK

    Kaska Porayska-Pomsta

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  8. University of Sussex, Brighton, UK

    Benedict du Boulay

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Chau, H., Barria-Pineda, J., Brusilovsky, P. (2018). Learning Content Recommender System for Instructors of Programming Courses. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-93846-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93845-5

  • Online ISBN: 978-3-319-93846-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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