Blending Measures of Programming and Social Behavior into Predictive Models of Student Achievement in Early Computing Courses

Abstract

Analyzing the process data of students as they complete programming assignments has the potential to provide computing educators with insights into both their students and the processes by which they learn to program. In prior research, we explored the relationship between (a) students’ programming behaviors and course outcomes, and (b) students’ participation within an online social learning environment and course outcomes. In both studies, we developed statistical measures derived from our data that significantly correlate with students’ course grades. Encouraged both by social theories of learning and a desire to improve the accuracy of our statistical models, we explore here the impact of incorporating our predictive measure derived from social behavior into three separate predictive measures derived from programming behaviors. We find that, in combining the measures, we are able to improve the overall predictive power of each measure. This finding affirms the importance of social interaction in the learning process, and provides evidence that predictive models derived from multiple sources of learning process data can provide significantly better predictive power by accounting for multiple factors responsible for student success.