Student network analysis: A novel way to predict delayed graduation in higher education

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings
EditorsSeiji Isotani, Eva Millán, Amy Ogan, Bruce McLaren, Peter Hastings, Rose Luckin
PublisherSpringer Verlag
Pages370-382
Number of pages13
ISBN (Print)9783030232030
DOIs
Publication statusPublished - 2019
Event20th International Conference on Artificial Intelligence in Education, AIED 2019 - Chicago, United States
Duration: 2019 Jun 252019 Jun 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11625 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Artificial Intelligence in Education, AIED 2019
Country/TerritoryUnited States
CityChicago
Period19/6/2519/6/29

Bibliographical note

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

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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