Which one's more work? Predicting effective credit hours between courses

Abstract

University students select courses for an upcoming term in part based on expected workload. Course credit hours is often the only metric given by the institution relevant to how much work a course will be and does not serve as a precise estimate due to the lack of granularity of the metric which can lead to student under or overestimation. We define a novel task of predicting relative effective course credit hours, or time load; essentially, determining which courses take more time than others. For this task, we draw from institutional data sources including course catalog descriptions, student enrollment histories and ratings from a popular course rating website. To validate this work, we design a personalized survey for university students to collect ground truth labels, presenting them with pairs of courses they had taken and asking which course took more time per week on average. We evaluate which sources of data using which machine representation techniques provide the best prediction of these course time load ratings. We establish a benchmark accuracy of 0.71 on this novel task and find skip-grams applied to enrollment data (i.e., course2vec), not catalog descriptions, to be most useful in predicting the time demands of a course.