Effective Machine Learning Algorithms Using Procrastination Indicators to Predict Task Completion Rates – A Literature Review

Abstract

Academic procrastination is an issue many students face, and it can have consequences to students’ mental health and quality of work. Machine learning algorithms can be used to predict the effects of academic procrastination on task completion; however, these algorithms need features that are good indicators of procrastination. Procrastination indicators used by previous studies include student self-ratings of academic procrastination, activity logs, time-stamped trace data of studying, and interaction behavior of computer-based learning environments. Previous studies explore the use of new technology, apps, and virtual learning assistants to monitor and predict procrastination-related behaviors. These studies use techniques such process mining, unsupervised machine learning, and probabilistic mixture models to identify student behavior patterns and connection to academic outcomes. For identifying the relationship between procrastination and academic performance, qualitative data (e.g., detailed student activity logs) was found more valuable than quantitative data (e.g., timestamped interactions, with computer-based learning environments). The next step of this research is to design an experiment and collect student procrastination indicator data that can be used to develop a random forest algorithm to predict task completion rates.