Course evaluation modeling using multi-level regression: a case study for a Master of Business Administration

Understanding and modelling course evaluation scores in higher education programs is crucial for enhancing and improving student experience and program outputs. This study investigates the influence of several factors related to course and instructor characteristics on the course evaluation scores in the Master of Business Administration (MBA) program. Course evaluation data from 1,639 course sections across 9 terms at a North American MBA program was used. The study utilized a multi-level regression model, which considers the hierarchal structure of the data and group-level heterogeneity, to model the course evaluation scores. The results show that larger class sizes correlate statistically with a lower evaluation score. Moreover, online classes received statistically lower evaluation scores than hybrid courses, which can be attributed to students’ feelings of isolation due to reduced face-to-face connection. Permanent faculty tend to have a higher average evaluation score compared with sessional faculty. This research advances understanding of factors influencing MBA course evaluations and provides evidence-based guidance for improving teaching effectiveness and program design in professional graduate education.