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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2016: Advances in Knowledge Discovery and Data Mining pp 89–101Cite as

Grade Prediction with Course and Student Specific Models

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9651))

Abstract

The accurate estimation of students’ grades in future courses is important as it can inform the selection of next term’s courses and create personalized degree pathways to facilitate successful and timely graduation. This paper presents future-course grade predictions methods based on sparse linear models and low-rank matrix factorizations that are specific to each course or student-course tuple. These methods identify the predictive subsets of prior courses on a course-by-course basis and better address problems associated with the not-missing-at-random nature of the student-course historical grade data. The methods were evaluated on a dataset obtained from the University of Minnesota. This evaluation showed that the course specific models outperformed various competing schemes with the best performing scheme achieving a RMSE across the different courses of 0.632 vs 0.661 for the best competing method.

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Aknowledgements

This work was supported in part by NSF (IIS-0905220, OCI-1048018, CNS-1162405, IIS-1247632, IIP-1414153, IIS-1447788) and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute. http://www.msi.umn.edu.

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Authors and Affiliations

  1. University of Minnesota, Minneapolis, USA

    Agoritsa Polyzou & George Karypis

Authors
  1. Agoritsa Polyzou
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  2. George Karypis
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Corresponding author

Correspondence to Agoritsa Polyzou .

Editor information

Editors and Affiliations

  1. The University of Melbourne, Melbourne, Victoria, Australia

    James Bailey

  2. The University of Texas at Dallas, Richardson, Texas, USA

    Latifur Khan

  3. Osaka University, Osaka, Japan

    Takashi Washio

  4. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  5. Shenzhen University, Shenzhen, China

    Joshua Zhexue Huang

  6. Massey University, Auckland, New Zealand

    Ruili Wang

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© 2016 Springer International Publishing Switzerland

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Polyzou, A., Karypis, G. (2016). Grade Prediction with Course and Student Specific Models. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9651. Springer, Cham. https://doi.org/10.1007/978-3-319-31753-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-31753-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31752-6

  • Online ISBN: 978-3-319-31753-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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