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Spatial abilities have been shown to have high predictability in students’ success in STEM related fields. Studies have also shown that there is a correlation between students’ spatial skills and programming abilities, but it is unknown how well students’ prior spatial abilities can predict students’ introductory programming abilities at the end of the semester. During this study we used a multinomal logistic regression to create a predictive model to predict students’ introductory programming abilities at the end of the semester. The highest model accuracy (64.6%) was obtained when accounting for students’ prior programming abilities, prior spatial skills, socioeconomic status, and three factors regarding students’ attitudes towards computing. It was also found that when looking at the predictability of each individual variable, students’ prior spatial ability had the highest predictability (56.6% accuracy) when compared to all other variables. 

This paper discusses the results of replicating and extending a study performed by Cooper et al. examining the relationship between students’ spatial skills and their success in learning to program. Whereas Cooper et al. worked with high school students participating in a summer program, we worked with college students taking an introductory computing course. Like Cooper et al.’s study, we saw a correlation between a student’s spatial skills and their success in learning computing. More significantly, we saw that after applying an intervention to teach spatial skills, students demonstrated improved performance both on a standard spatial skills assessment as well as on a CS content instrument. We also saw a correlation between students’ enjoyment in computing and improved performance both on a standard spatial skills assessment and on a CS content instrument, a result not observed by Cooper et al. 

Cognitive tests have been long used as a measure of student knowledge, ability, and as a predictor for success in engineering and computer science. However, these tests are not without their own problems relating to priming, difficulty (resulting in test fatigue) and time on exam. This paper discusses efforts to modify Parker et al.'s Second CS1 aptitude test (SCS1) \citeParker16 to reduce the time spent on the exam, provide greater customization to match concepts taught across three universities, and reduce redundancy of test questions all while maintaining the instrument's reliability. This instrument was modified for use on an ongoing grant investigating whether spatial abilities impact the success of students in introductory CS courses. The instrument developed in this paper is a revised shortened version of Second Computer Science 1 (SCS1) aptitude test, designated as SCS1R. 

We present a prediction model to detect delayed graduation cases based on student network analysis. In the U.S. only 60% of undergraduate students finish their bachelors’ degrees in 6 years [1]. We present many features based on student networks and activity records. To our knowledge, our feature design, which includes conventional academic performance features, student network features, and fix-point features, is one of the most comprehensive ones. We achieved the F-1 score of 0.85 and AUCROC of 0.86. 

Female, Black, Latinx, Native American, low-income, and rural students remain underrepresented among computer science undergraduate degree recipients. Along with student, family, and secondary school characteristics, college organizational climate, curricula, and instructional practices shape undergraduates’ experiences that foster persistence until graduation. Our quasi-experimental project, Improving the Persistence and Success of Students from Underrepresented Populations in Computer Science (I-PASS), is designed to augment students’ persistence until they earn their computer science degree. Drawing on prior research, including Tinto's model of effective institutional actions for retention, I-PASS Scholars—all low-income, female and/or members of underserved demographics groups— receive a four-year scholarship; mentoring, tutoring, advising; and opportunities to integrate into the academic and social life of the campus. Students’ written reflections and attitude surveys suggest I-PASS's components foster their retention by, among other mechanisms, enhancing their computer science identity development and sense of belonging in the major.