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Title: Developing an early-warning system for spotting at-risk students by using eBook interaction logs
Authors: Akçapınar, Gökhan
Hasnine, Mohammad Nehal
Majumdar, Rwitajit
Flanagan, Brendan
Ogata, Hiroaki
Author's alias: 緒方, 広明
Keywords: Early-warning systems
At-risk students
Educational data mining
Learning analytics
Academic performance prediction
Issue Date: 10-May-2019
Publisher: Springer Nature
Journal title: Smart Learning Environments
Volume: 6
Thesis number: 4
Abstract: 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.
Rights: © The Author(s). 2019
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
URI: http://hdl.handle.net/2433/242865
DOI(Published Version): 10.1186/s40561-019-0083-4
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