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Sequence-Based Approaches to Course Recommender Systems

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Book cover Database and Expert Systems Applications (DEXA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11029))

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Abstract

The scope and order of courses to take to graduate are typically defined, but liberal programs encourage flexibility and may generate many possible paths to graduation. Students and course counselors struggle with the question of choosing a suitable course at a proper time. Many researchers have focused on making course recommendations with traditional data mining techniques, yet failed to take a student’s sequence of past courses into consideration. In this paper, we study sequence-based approaches for the course recommender system. First, we implement a course recommender system based on three different sequence related approaches: process mining, dependency graph and sequential pattern mining. Then, we evaluate the impact of the recommender system. The result shows that all can improve the performance of students while the approach based on dependency graph contributes most.

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Authors and Affiliations

  1. University of Alberta, Edmonton, Canada

    Ren Wang & Osmar R. Zaïane

Authors
  1. Ren Wang
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  2. Osmar R. Zaïane
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Corresponding author

Correspondence to Osmar R. Zaïane .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Victoria University of Wellington, Wellington, New Zealand

    Hui Ma

  3. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  4. University of Regensburg, Regensburg, Germany

    Günther Pernul

  5. Johannes Kepler University, Linz, Austria

    Roland R. Wagner

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Wang, R., Zaïane, O.R. (2018). Sequence-Based Approaches to Course Recommender Systems. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11029. Springer, Cham. https://doi.org/10.1007/978-3-319-98809-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-98809-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98808-5

  • Online ISBN: 978-3-319-98809-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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