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Student Network Analysis: A Novel Way to Predict Delayed Graduation in Higher Education

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Book cover Artificial Intelligence in Education (AIED 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11625))

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Abstract

We present a prediction model to detect delayed graduation cases based on student network analysis. In the U.S. only 60% of undergraduate students finish their bachelors’ degrees in 6 years [1]. We present many features based on student networks and activity records. To our knowledge, our feature design, which includes conventional academic performance features, student network features, and fix-point features, is one of the most comprehensive ones. We achieved the F-1 score of 0.85 and AUCROC of 0.86.

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Notes

  1. 1.

    The Jaccard index is a popular node similarity metric in networks based on the number of common neighbors divided by the sum of all neighbors.

  2. 2.

    This is a very important fact about our network dentition. We do not focus on only courses but also many other aspects of academic life.

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Acknowledgements

This work is supported by the National Science Foundation under Grant No. 1820862. Noseong Park and Mohsen Dorodchi are the co-corresponding authors.

Author information

Authors and Affiliations

  1. University of North Carolina at Charlotte, Charlotte, NC, USA

    Nasheen Nur, Mohsen Dorodchi, Wenwen Dou, Mohammad Javad Mahzoon, Xi Niu & Mary Lou Maher

  2. George Mason University, Fairfax, VA, USA

    Noseong Park

Authors
  1. Nasheen Nur
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  2. Noseong Park
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  3. Mohsen Dorodchi
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  4. Wenwen Dou
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  5. Mohammad Javad Mahzoon
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  6. Xi Niu
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  7. Mary Lou Maher
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Corresponding author

Correspondence to Noseong Park .

Editor information

Editors and Affiliations

  1. University of Sao Paulo, Sao Paulo, Brazil

    Seiji Isotani

  2. University of Malaga, Málaga, Spain

    Eva Millán

  3. Carnegie Mellon University, Pittsburgh, PA, USA

    Amy Ogan

  4. DePaul University, Chicago, IL, USA

    Peter Hastings

  5. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  6. University College London, London, UK

    Rose Luckin

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Nur, N. et al. (2019). Student Network Analysis: A Novel Way to Predict Delayed Graduation in Higher Education. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11625. Springer, Cham. https://doi.org/10.1007/978-3-030-23204-7_31

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  • DOI: https://doi.org/10.1007/978-3-030-23204-7_31

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