Towards Using Unsupervised Learning for Comparing Traditional and Synchronous Online Learning in Assessing Students' Academic Performance

Understanding students' learning processes and education-related phenomena by extracting knowledge from educational data sets represents a continuous interest in the educational data mining domain. Due to an accelerated expansion of online learning and digitalisation in education, there is a growing interest in understanding the impact of online learning on the academic performance of students. In this study, we comparatively investigate traditional and synchronous online learning methods to assess students' performance through the use of deep autoencoders. Experiments performed on real data sets collected in both online and traditional learning environments showed that autoencoders are able to detect hidden patterns in academic data sets unsupervised; these patterns are valuable for the prediction of students' performance. The obtained results emphasized that, for the considered case studies, traditional evaluations are a little more accurate than online evaluations. Still, after applying a one-tailed paired Wilcoxon signed-rank test, no statistically significant difference between the traditional and online evaluations was observed.

Automated summary

By comparing traditional and synchronous online learning methods, this study found that autoencoders can detect hidden patterns in academic data sets unsupervised and are valuable for predicting students' performance. The results showed that while traditional evaluations are slightly more accurate than online evaluations, there was no statistically significant difference between the two types of assessments.