Developing an early-warning system for spotting at-risk students by using eBook interaction logs

AKÇAPINAR G., Hasnine M. N. , Rwitajit M., Flanagan B., Ogata H.

Smart Learning Environments, vol.6, 2019 (Peer-Reviewed Journal) identifier

  • Publication Type: Article / Article
  • Volume: 6
  • Publication Date: 2019
  • Doi Number: 10.1186/s40561-019-0083-4
  • Journal Name: Smart Learning Environments
  • Journal Indexes: Emerging Sources Citation Index, Scopus, INSPEC, Directory of Open Access Journals
  • Keywords: Early-warning systems, At-risk students, Educational data mining, Learning analytics, Academic performance prediction, PERFORMANCE, PREDICTION, ONLINE, MODELS


Early prediction systems have already been applied successfully in various educational contexts. In this study, we investigated developing an early prediction system in the context of eBook-based teaching-learning and used students' eBook reading data to develop an early warning system for students at-risk of academic failure -students whose academic performance is low. To determine the best performing model and optimum time for possible interventions we created prediction models by using 13 prediction algorithms with the data from different weeks of the course. We also tested effects of data transformation on prediction models. 10-fold cross-validation was used for all prediction models. Accuracy and Kappa metrics were used to compare the performance of the models. Our results revealed that in a sixteen-week long course all models reached their highest performance with the data from the 15th week. On the other hand, starting from the 3rd week, the models classified low and high performing students with an accuracy of over 79%. In terms of algorithms, Random Forest (RF) outperformed other algorithms when raw data were used, however, with the transformed data J48 algorithm performed better. When categorical data were used, Naive Bayes (NB) outperformed other algorithms. Results also indicated that models with transformed data performed lower than the models created using categorical data. However, models with categorical data showed similar performance with models with raw data. The implications of the results presented in this research were also discussed with respect to the field of Learning Analytics.