Prediction of Dilatory Behavior in eLearning: A Comparison of Multiple Machine Learning Models
This work addresses the problem of predicting dilatory behavior for online learners, but it is incremental as it compares existing methods on new data without introducing novel techniques.
The study tackled predicting procrastination in online learning by comparing machine learning models using subjective and objective predictors, finding that objective predictors consistently outperformed subjective ones and combined predictors performed slightly better, with Gradient Boosting Machines, Bayesian multilevel models, and Random Forest being the best for each predictor type respectively.
Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems and learning analytics, indicators of such behavior can be detected, enabling predictions of future procrastination and other dilatory behavior. However, research focusing on such predictions is scarce. Moreover, studies involving different types of predictors and comparisons between the predictive performance of various methods are virtually non-existent. In this study, we aim to fill these research gaps by analyzing the performance of multiple machine learning algorithms when predicting the delayed or timely submission of online assignments in a higher education setting with two categories of predictors: subjective, questionnaire-based variables and objective, log-data based indicators extracted from a learning management system. The results show that models with objective predictors consistently outperform models with subjective predictors, and a combination of both variable types perform slightly better. For each of these three options, a different approach prevailed (Gradient Boosting Machines for the subjective, Bayesian multilevel models for the objective, and Random Forest for the combined predictors). We conclude that careful attention should be paid to the selection of predictors and algorithms before implementing such models in learning management systems.