TY - GEN
T1 - Using submission log data to investigate novice programmers' employment of debugging strategies
AU - Liu, Qianhui
AU - Paquette, Luc
N1 - This study is funded by National Science Foundation Award #1942962.
PY - 2023/3/13
Y1 - 2023/3/13
N2 - Debugging is a distinct subject in programming that is both comprehensive and challenging for novice programmers. However, instructors have limited opportunities to gain insights into the difficulties students encountered in isolated debugging processes. While qualitative studies have identified debugging strategies that novice programmers use and how they relate to theoretical debugging frameworks, limited larger scale quantitative analyses have been conducted to investigate how students' debugging behaviors observed in log data align with the identified strategies and how they relate to successful debugging. In this study, we used submission log data to understand how the existing debugging strategies are employed by students in an introductory CS course when solving homework problems. We identified strategies from existing debugging literature that can be observed with trace data and extracted features to reveal how efficient debugging is associated with debugging strategy usage. Our findings both align with and contradict past assumptions from previous studies by suggesting that minor code edition can be a beneficial strategy and that width and depth aggregations of the same debugging behavior can reveal opposite effects on debugging efficiency.
AB - Debugging is a distinct subject in programming that is both comprehensive and challenging for novice programmers. However, instructors have limited opportunities to gain insights into the difficulties students encountered in isolated debugging processes. While qualitative studies have identified debugging strategies that novice programmers use and how they relate to theoretical debugging frameworks, limited larger scale quantitative analyses have been conducted to investigate how students' debugging behaviors observed in log data align with the identified strategies and how they relate to successful debugging. In this study, we used submission log data to understand how the existing debugging strategies are employed by students in an introductory CS course when solving homework problems. We identified strategies from existing debugging literature that can be observed with trace data and extracted features to reveal how efficient debugging is associated with debugging strategy usage. Our findings both align with and contradict past assumptions from previous studies by suggesting that minor code edition can be a beneficial strategy and that width and depth aggregations of the same debugging behavior can reveal opposite effects on debugging efficiency.
KW - Computer Science education
KW - Debugging strategies
KW - Submission log data
UR - http://www.scopus.com/inward/record.url?scp=85149270283&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85149270283&partnerID=8YFLogxK
U2 - 10.1145/3576050.3576094
DO - 10.1145/3576050.3576094
M3 - Conference contribution
AN - SCOPUS:85149270283
T3 - ACM International Conference Proceeding Series
SP - 637
EP - 643
BT - LAK 2023 Conference Proceedings - Towards Trustworthy Learning Analytics - 13th International Conference on Learning Analytics and Knowledge
PB - Association for Computing Machinery
T2 - 13th International Conference on Learning Analytics and Knowledge: Towards Trustworthy Learning Analytics, LAK 2023
Y2 - 13 March 2023 through 17 March 2023
ER -