Studying the factors that can lead to student success and persistence has been the subject of a large body of CS education research. Our earlier work on tools to engage students in Algorithms and Data Structures courses demonstrated uneven benefits, especially among underrepresented students. Using a national pool of students from 13 colleges/universities in Algorithms and Data Structures courses, we used pre and post online surveys to assess the psychometric characteristics of a revised measure of prerequisite proficiency together with 4 others (confidence in computing, precollege computing experiences, skill with computer applications and course engagement) to test for gender and ethnic differences and to investigate the factors that would be useful in predicting final course grade. We were able to confirm differences in prerequisite proficiency, however, differences were a characteristic of additional factors such as precollege experiences, self-rated skill levels and confidence in computing. Regression analysis showed these differences can be mitigated by directing efforts at multiple components, rather than just prerequisite proficiency. Measures taken in the post-survey at the end of the semester showed a reduced number of demographic differences, when compared to the pre-survey. Prerequisite proficiency and performance scores were found to be significant predictors of course grades.
Goolkasian et al. (Thu,) studied this question.