EPARS: Early Prediction of At-Risk Students with Online and Offline Learning Behaviors

Yu YANG*, Zhiyuan WEN, Jiannong CAO, Jiaxing SHEN, Hongzhi YIN, Xiaofang ZHOU

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

3 Citations (Scopus)

Abstract

Early prediction of students at risk (STAR) is an effective and significant means to provide timely intervention for dropout and suicide. Existing works mostly rely on either online or offline learning behaviors which are not comprehensive enough to capture the whole learning processes and lead to unsatisfying prediction performance. We propose a novel algorithm (EPARS) that could early predict STAR in a semester by modeling online and offline learning behaviors. The online behaviors come from the log of activities when students use the online learning management system. The offline behaviors derive from the check-in records of the library. Our main observations are two folds. Significantly different from good students, STAR barely have regular and clear study routines. We devised a multi-scale bag-of-regularity method to extract the regularity of learning behaviors that is robust to sparse data. Second, friends of STAR are more likely to be at risk. We constructed a co-occurrence network to approximate the underlying social network and encode the social homophily as features through network embedding. To validate the proposed algorithm, extensive experiments have been conducted among an Asian university with 15, 503 undergraduate students. The results indicate EPARS outperforms baselines by 14.62%–38.22% in predicting STAR.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings
EditorsYunmook NAH, Bin CUI, Sang-Won LEE, Jeffrey Xu YU, Yang-Sae MOON, Steven Euijong WHANG
PublisherSpringer, Cham
Pages3-19
Number of pages17
Volume2
ISBN (Print)9783030594152
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event25th International Conference on Database Systems for Advanced Applications, DASFAA 2020 - Jeju, Korea, Republic of
Duration: 24 Sep 202027 Sep 2020

Publication series

NameLecture Notes in Computer Science
Volume12113
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Database Systems for Advanced Applications, DASFAA 2020
Country/TerritoryKorea, Republic of
CityJeju
Period24/09/2027/09/20

Bibliographical note

This research has been supported by the PolyU Teaching Development (Grant No. 1.61.xx.9A5V) and ARC Discovery Project (Grant No. DP190101985, DP170103954 and DP170101172).

Keywords

  • At-risk student prediction
  • Learning analytics
  • Learning behavior
  • Regularity patterns
  • Social homophily

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