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

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 de...

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Bibliographic Details
Published inArtificial Intelligence in Education Vol. 11625; pp. 370 - 382
Main Authors Nur, Nasheen, Park, Noseong, Dorodchi, Mohsen, Dou, Wenwen, Mahzoon, Mohammad Javad, Niu, Xi, Maher, Mary Lou
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 01.01.2019
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Network analysis
Risk prediction
Student data
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Summary: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.
ISBN:9783030232030
3030232034
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-23204-7_31