Geodesic
Predicting Dropout in Online Learning Environments
Computer Science and Information Systems, Tome 18 (2021) no. 3.

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Online learning environments became popular in recent years. Due to high attrition rates, the problem of student dropouts became of immense importance for course designers, and course makers. In this paper, we utilized lasso and ridge logistic regression to create a prediction model for dropout on the Open University database. We investigated how early dropout can be predicted, and why dropouts occur. To answer the first question, we created models for eight different time frames, ranging from the beginning of the course to the mid-term. There are two results based on two definitions of dropout. Results show that at the beginning AUC of the prediction model is 0.549 and 0.661 and rises to 0.681 and 0.869 at mid-term. By analyzing logistic regression coefficients, we showed that at the beginning of the course demographic features of the student and course description features are the most important variables for dropout prediction, while later student activity gains more importance.
Keywords: Education Data Mining, Learning Analytics, Dropout prediction, Lasso, and Ridge Logistic Regression 
@article{CSIS_2021_18_3_a15,
     author = {Sandro Radovanovi\'c and Boris Deliba\v{s}i\'c and Milija Suknovi\'c},
     title = {Predicting {Dropout} in {Online} {Learning} {Environments}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {18},
     number = {3},
     year = {2021},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2021_18_3_a15/}
}
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Sandro Radovanović; Boris Delibašić; Milija Suknović. Predicting Dropout in Online Learning Environments. Computer Science and Information Systems, Tome 18 (2021) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2021_18_3_a15/