Predicting student performance using sequence classification with time-based windows

Abstract

A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula. Furthermore, the recent changes caused by the COVID-19 pandemic have led to a drastic increase in importance and ubiquity of online education. Among the major advantages of e-learning is not only improving students' learning experience and widening their educational prospects, but also an opportunity to gain insights into students' learning processes with learning analytics. This study contributes to the topic of improving and understanding e-learning processes in the following ways. First, we demonstrate that accurate predictive models can be built based on sequential patterns derived from students' behavioral data, which are able to identify underperforming students early in the course. Second, we investigate the specificity-generalizability trade-off in building such predictive models by investigating whether predictive models should be built for every course individually based on course-specific sequential patterns, or across several courses based on more general behavioral patterns. Finally, we present a methodology for capturing temporal aspects in behavioral data and analyze its influence on the predictive performance of the models. The results of our improved sequence classification technique are capable to predict student performance with high levels of accuracy, reaching 90% for course-specific models.

Keywords: Behavioral patterns; Feature engineering; Machine learning; Sequence mining; Success prediction.

Fig. 1

An example of behavioral data and its transformations needed to perform a sequential data analysis.

Fig. 2

An illustration of the sequence classification process with iBCM.

Fig. 3

An example of the situation where the information regarding temporal aspect could be substantial for predicting student performance.

Fig. 4

Top 15 most important sequential patterns found by iBCM based on mean decreased impurity of the best-performing Random Forest model built for the ENMAN303 dataset. The interpretation of the events is as follows: $0-enrollment.activated$, $1-course.upgrade.displayed$, $2-ui.lms.link\text{\_}clicked$, $4-edx.ui.lms.sequence.nex{t}_{s}elected$, $5-load\text{\_}video$, $6-page\text{\_}close$, $10-stop\text{\_}video$, $11-problem\text{\_}check$, $12-problem\text{\_}submitted$, $13-problem\text{\_}graded$, $15-ui.lms.sequence.previous\text{\_}selected$, $16-ui.lms.sequence.tab\text{\_}selected$, $19-openassessment.upload\text{\_}file$.