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.
Highlights
Digital learning materials especially digital textbooks is a core part of modern education, and the adoption of digital textbooks in education is increasing
This study defines at-risk students whose academic performance is less than 50% of the class based on their in-class exercises and final exam score
We investigated whether digital textbook usage data can be used to develop an early warning system for students at-risk of academic failure
Summary
Digital learning materials especially digital textbooks is a core part of modern education, and the adoption of digital textbooks in education is increasing. Villagrá-Arnedo et al (2017)‘s study argued that to improve students’ academic performance, having knowledge on their actual progress and trying to predict their outcome at the earliest stages of the learning process can be extremely helpful to act early and cut off the problems at the root. To provide a meaningful guide to teachers and students, this study attempted to detect study trends and behavior patterns and to identify the causes of learning problems. To overcome this problem, a black box approach is used to develop prediction models. Their study developed a set of graphical tools is to interpret the output information
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