How prior programming experience affects students’ pair programming experiences and outcomes

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Abstract

Pair programming is a collaborative learning approach in computer science in which students (or employees) work closely with a partner on the same programming task. A long-standing question within pair programming is whether certain combinations of students lead to greater learning, effort, and/or performance. Earlier studies have explored the role of prior programming experience, including the discrepancy between partners’ experience, as a potentially important factor in shaping these outcomes. However, the previous findings are highly inconsistent, which may result from divergent (and often suboptimal) ways of defining previous experience or skill, problems with self-selection into pairs, and small sample sizes that often yield nonsignificant results. The present study sought to improve on all of these limitations through an examination of 587 undergraduates who each participated in three different randomly assigned pairings. Not surprisingly, students’ own programming experience was positively related to understanding concepts from lab, confidence in the finished product, and overall interest in computer science. However, students who worked with a more experienced partner actually had poorer outcomes, including lower effort exerted on the assignment, perceptions that their partner gave more effort than they did, less time in the driving role (i.e., typing out the assignment), lower understanding of concepts from lab, and less interest in computer science overall. The partner’s experience is unrelated to other outcomes, including confidence in the finished assignment, feeling productive during lab session, and average grades received during the pairing. These results provide important considerations for the assignment of students to pairs.

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APA

Bowman, N. A., Jarratt, L., Culver, K. C., & Segre, A. M. (2019). How prior programming experience affects students’ pair programming experiences and outcomes. In Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE (pp. 170–175). Association for Computing Machinery. https://doi.org/10.1145/3304221.3319781

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