Exploring The Relationship Between Novice Programmer Confusion And Achievement

Using a discovery-with-models approach, we study the relationships between novice Java programmers' experiences of confusion and their achievement, as measured through their midterm examination scores. Two coders manually labeled samples of student compilation logs with whether they represent a student who was confused. From the labeled data, we built a model that we used to label the entire data set. We then analysed the relationship between patterns of confusion and non-confusion over time, and students' midterm scores. We found that, in accordance with prior findings, prolonged confusion is associated with poorer student achievement. However, confusion which is resolved is associated with statistically significantly better midterm performance than never being confused at all.