Comparative Analysis of Student Performance Across Different Cohorts in Higher Education

Tracking student progress throughout their coursework is a common topic in educational research. While valuable insights can be gained from analyzing learner data, such analysis can sometimes be misleading, particularly when it involves predicting students’ final course achievements or drawing generalized conclusions from these predictions that do not account for individual student engagement.

The study presented by BMU authors Emilija Kisić, Miroslava Raspopović Milić, Jovana Jović, Nemanja Zdravković and Faruk Selimović, at the eLearning 2024 conference analyzes learner data from two different student cohorts that attended the same course in two different academic years. The focus of the paper is placed on identifying patterns in student engagement, similarities between two cohorts and exploring individual differences of learners. The interpretation of student activities sequences was implemented using sequence plotting and heatmaps, while Ward method was used for hierarchical clustering.

The study aimed to understand the extent of similarities and differences in learning behavior across the cohorts, providing insights into how students interact with course material over a semester. Results show that there are many similarities between two cohorts, however, when expressing individual differences of each learner it was concluded that none of the students had the same sequence of engagement as the cluster’s mean.

Check out the full paper:

Emilija Kisić, Miroslava Raspopović Milić, Jovana Jović, Nemanja Zdravković, Faruk Selimović (2024). Comparative Analysis of Student Performance Across Different Cohorts in Higher Education. eLearning 2024. 24-31