Prior Programming Experience: A Persistent Performance Gap in CS1 and CS2

被引:7
作者
Bui, Giang [1 ]
Sibia, Naaz [1 ]
Bernuy, Angela Zavaleta [2 ]
Liut, Michael [1 ]
Petersen, Andrew [1 ]
机构
[1] Univ Toronto, Mississauga, ON, Canada
[2] Univ Toronto, Toronto, ON, Canada
来源
PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 1, SIGCSE 2023 | 2023年
关键词
Prior Experience; Prediction; Confidence; Self-efficacy; CS1; CS2;
D O I
10.1145/3545945.3569752
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Previous work has reported on the advantageous effects of prior experience in CS1, but it remains unclear whether these effects fade over a sequence of introductory programming courses. Furthermore, while student perceptions suggest that prior experience remains important, studies have reported that a student's expectation of their performance is a more accurate predictor of outcome. We aim to confirm if prior experience (formal or informal) provides short-term and long-term advantages in computing courses or if the advantage fades. Furthermore, we explore whether the expectation of performance is a more accurate predictor of student success than informal and formal prior experience. To explore these questions, we deployed surveys in a CS1 course to gauge students' level of prior experience in programming, prediction of final exam grades, and self-efficacy to succeed in university. Grades from CS1 and CS2 were also collected. We observed a persistent (1-letter grade) gap between the performance of students with no prior experience and those with any experience, but we did not observe a noteworthy gap when comparing student performance based on formal or informal experience. We also observed differences in self-efficacy and retention rates between different levels of prior experience. Lastly, we confirm that success in CS1 can be better reflected and predicted by some controllable factors, such as students' perceptions of ability.
引用
收藏
页码:889 / 895
页数:7
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