A Simple, Language-Independent Approach to Identifying Potentially At-Risk Introductory Programming Students

Abstract

For decades computing educators have been trying to identify and predict at-risk students, particularly early in the first programming course. These efforts range from the analyzing demographic data that pre-exists undergraduate entrance to using instruments such as concept inventories, to the analysis of data arising during education. Such efforts have had varying degrees of success, have not seen widespread adoption, and have left room for improvement. We analyse results from a two-year study with several hundred students in the first year of programming, comprising majors and non-majors. We find evidence supporting a hypothesis that engagement with extra credit assessment provides an effective method of differentiating students who are not at risk from those who may be. Further, this method can be used to predict risk early in the semester, as any engagement – not necessarily completion – is enough to make this differentiation. Additionally, we show that this approach is not dependent on any one programming language. In fact, the extra credit opportunities need not even involve programming. Our results may be of interest to educators, as well as researchers who may want to replicate these results in other settings.