University undergraduate course grades have several purposes: they provide feedback to the student and motivation to perform well; serve as admission criteria for entering a major; and are used as selection criteria for future employers and graduate programs. Accurate assignment of grades is therefore important and critical to ensure fairness. However, grades may also impact the student’s assessment of the instructor, which leads to a conflict of interest when such assessments are a component of employment, salary, or tenure decisions. This paper performs a detailed descriptive analysis of undergraduate grades collected over an eight year period from a major metropolitan university. Interesting grading patterns are identified and discussed, and the analysis suggests that grading policies vary substantially at the department, course, and instructor level. A connection is observed between course/department enrollment and average grades assigned. A particular focus of this study involves describing the grading behavior of instructors, with the goal of identifying instructors that assign grades that are statistically far above or below the norm. The analysis performed in this study can be applied to grade data from other universities using our publicly available Python-based analytics tool. The results of these analyses can be used to better understand existing grading policies, identify potential sources of grading inequities, and, when appropriate, take corrective action.

College students are required to select a major but are often provided with only a modest amount of support in making this important decision. A poor decision is detrimental to the student, since it may result in the student later switching to a different major with a delay in graduation—or even result in the student leaving the university. This also impacts the university since time to graduation and retention rate are used to evaluate the quality of a university. There is a general lack of research on recommender systems for college majors, with the most relevant systems focusing on course-level recommendations. This study describes and evaluates a recommender system for selecting an undergraduate major, utilizing nine years of historical student data from a large university. The system bases its recommendations on the courses that the student takes in the first few years of college, and how well they performed in these courses. The system is designed to recommend majors that the student is likely to be interested in and will perform well in. Recommendations are evaluated based on the likelihood that the student's actual major was in the top five recommended majors, and whether the student performed above average in that major. The recommendation system dramatically outperforms the baseline strategy of randomly selecting a major, and when the recommendation is followed the student is 12% more likely to perform above average in the major.