  Course load analytics (CLA) inferred from LMS and enrollment features can
offer a more accurate representation of course workload to students than credit
hours and potentially aid in their course selection decisions. In this study,
we produce and evaluate the first machine-learned predictions of student course
load ratings and generalize our model to the full 10,000 course catalog of a
large public university. We then retrospectively analyze longitudinal
differences in the semester load of student course selections throughout their
degree. CLA by semester shows that a student's first semester at the university
is among their highest load semesters, as opposed to a credit hour-based
analysis, which would indicate it is among their lowest. Investigating what
role predicted course load may play in program retention, we find that students
who maintain a semester load that is low as measured by credit hours but high
as measured by CLA are more likely to leave their program of study. This
discrepancy in course load is particularly pertinent in STEM and associated
with high prerequisite courses. Our findings have implications for academic
advising, institutional handling of the freshman experience, and student-facing
analytics to help students better plan, anticipate, and prepare for their
selected courses.
