Prediction of Dilatory Behavior in eLearning: A Comparison of Multiple Machine Learning Models

TL;DR

This study compares multiple machine learning models to predict procrastination in online learning, finding that objective data outperforms subjective questionnaires, with combined predictors offering slight improvements.

Contribution

It provides a comparative analysis of different machine learning algorithms and predictor types for predicting dilatory behavior in eLearning environments.

Findings

Objective predictors outperform subjective ones.

Different models excel with different predictor types.

Combining predictor types yields marginally better predictions.

Abstract

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.