Insights into undergraduate pathways using course load analytics

Compared to K-12, US institutions of higher education, particularly four-year universities, give students a high amount of elective course choice. This choice comes with unique challenges that can inhibit their learning path, such as the choice to overload on credit hours causing early undergraduate dropout among older students with prior vocational training and completed degrees [22]. Conversely, low enrollment levels have also been found to be associated with worse educational outcomes, potentially due to a lack of financial and academic support [5]. These findings, though seemingly contradictory, suggest that semester workload may play an important role in explaining the complicated story of student success in higher education. However, recent work has found that credit hours is not a suitable proxy for course workload, as it captures only 6% of the variance in student reported course load compared to 36% captured by LMS features [36]. In this paper, we introduce course load analytics (CLA) as a machine learning approach to producing metrics about course workload relevant to student course selection. This work is the first to predict course load at scale, generalizing to over 10,000 courses at a large public institution and going beyond time load considerations by incorporating more holistic measures such as mental effort and psychological stress. Our findings suggest that the discrepancy between anticipated course load (i.e., as calculated by credit hours) and actual course load (i.e., as estimated by CLA) may be a significant factor in program stop-out.