Can Interaction Patterns with Supplemental Study Tools Predict Outcomes in CS1?
Anthony Estey, Yvonne Coady · 2016
Recent research suggests that one-third of the students enrolled in CS1 courses typically end up failing. Several studies have demonstrated how learning tools can assist struggling students. This work presents the evolution of a practice tool co-designed with student input. BitFit was developed to (1) provide students with an environment to practice weekly material and receive support when needed; and (2) collect student usage data as students progress through programming exercises. Our analysis of 652 students over three semesters highlights a number of predictors for success. Our findings support recent studies that suggest that at-risk students can be identified as early as two weeks into the semester; this group accounted for almost 30% of the students who failed the course in our study. Our results also reveal that interaction patterns with BitFit, in particular with hint features requested by students, allow the identification of another 52% of students who eventually fail. Throughout the semester, students who failed the course used hint features four times as often as top students, while only attempting to compile code one-third as often. The combination of early indicators and interaction patterns identify 81% of students who failed the course during our study.