This survey article aims to investigate the effects that prerequisite course proficiency has on course outcomes in an advanced data structure (ADS) course, specifically for Black, Latinx, Native American, and Pacific Islander (BLNPI) and transfer students in a university system. The study is motivated by the fact that BLNPI and female students “tend to have worse outcomes during their time in university compared to their majority counterparts.” Previous factors attributed to this include early exposure to computing, sense of belonging, and prerequisite knowledge indicated by prerequisite course grades. But it is not clear to what degree these factors affect student progress. New evidence shows that women/first-generation students enter into ADS courses with less prerequisite course proficiency than their majority counterparts.

The prerequisites for ADS for the 320 students surveyed were introduction to programming, data structures, discrete maths, advanced discrete maths, and computer organization. The data collected included preterm test scores, final exam scores, course grades, and prerequisite course grades. Since the courses were not equal with regard to difficulty level, normalization was done using z-scores. Also, nonparameteric tests were applied as the data were not in normal distribution.

Queries were designed for the survey and results: Is there a statistically significant difference between demographic pairs? Yes, transfer students performed significantly worse. Is there a consistent gap in prerequisite course performance versus ADS course performance in demographic pairs? No, with respect to gender; however, it holds in BLNPI versus transfer students. Is there a significant difference in course outcomes by gender, first generation, or BLNPI status? No, but transfer students performed worse in final outcomes. Is there a correlation between prerequisite course proficiency versus number of prerequisites taken? No.

The survey found that BLNPI students performed significantly worse in all prerequisite courses compared to their counterparts, but there was no correlation between course count and preterm course test scores. The study indicates that students in ADS courses were from widely varying levels of prerequisite subjects. A positive correlation was found between prerequisite course proficiency and success in final exams; those who lagged behind in prerequisite courses continued to incur gaps in knowledge. The demographics show no difference with regards to gender.

Limiting factors of the survey: generalizability is not possible due to the small group size, the likely subjectivity in grading, delays in the timely supply of course materials, likely inflated grades, and the single-course-based survey. Also, a preterm test cannot be a validated instrument due to no provision of incentive in its performance.

Final grades in ADS were 93.4 percent (average) and 96 percent (median); while this is an A grade, it is unlikely for the majority. This raises questions about how the courses are run and graded, and suggests that there should be a better measure of student understanding of course material. One possible fix is mastery learning, where a student “must master a unit before proceeding to the next one.” However, this may delay the academic calendar.

Overall, this interesting article paves the way for applying innovative methods and data analytics to the evaluation of student performance in academic programs.