Can Students' Spatial Skills Predict Their Programming Abilities?

Abstract

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.