TY - GEN
T1 - Why do CS1 Students Think They're Bad at Programming?
T2 - 16th Annual ACM Conference on International Computing Education Research, ICER 2020
AU - Gorson, Jamie
AU - O'Rourke, Eleanor
N1 - Funding Information:
We thank Rachel Trana for collaborating on the survey design and our community partners for providing access to students. We thank our Delta Lab colleagues for valuable discussions and feedback. This work was supported by the National Science Foundation under Grant IIS-1755628 and by the National Science Foundation Graduate Research Fellowship Program.
Publisher Copyright:
© 2020 ACM.
PY - 2020/8/10
Y1 - 2020/8/10
N2 - Undergraduate computer science (CS) programs often suffer from high dropout rates. Recent research suggests that self-efficacy - an individual's belief in their ability to complete a task - can influence whether students decide to persist in CS. Studies show that students' self-assessments affect their self-efficacy in many domains, and in CS, researchers have found that students frequently assess their programming ability based on their expectations about the programming process. However, we know little about the specific programming experiences that prompt the negative self-assessments that lead to lower self-efficacy. In this paper, we present findings from a survey study with 214 CS1 students from three universities. We used vignette-style questions to describe thirteen programming moments which may prompt negative self-assessments, such as getting syntax errors and spending time planning. We found that many students across all three universities reported that they negatively self-assess at each of the thirteen moments, despite the differences in curriculum and population. Furthermore, those who report more frequent negative self-assessments tend to have lower self-efficacy. Finally, our findings suggest that students' perceptions of professional programming practice may influence their expectations and negative self-assessments. By reducing the frequency that students self-assess negatively while programming, we may be able to improve self-efficacy and decrease dropout rates in CS.
AB - Undergraduate computer science (CS) programs often suffer from high dropout rates. Recent research suggests that self-efficacy - an individual's belief in their ability to complete a task - can influence whether students decide to persist in CS. Studies show that students' self-assessments affect their self-efficacy in many domains, and in CS, researchers have found that students frequently assess their programming ability based on their expectations about the programming process. However, we know little about the specific programming experiences that prompt the negative self-assessments that lead to lower self-efficacy. In this paper, we present findings from a survey study with 214 CS1 students from three universities. We used vignette-style questions to describe thirteen programming moments which may prompt negative self-assessments, such as getting syntax errors and spending time planning. We found that many students across all three universities reported that they negatively self-assess at each of the thirteen moments, despite the differences in curriculum and population. Furthermore, those who report more frequent negative self-assessments tend to have lower self-efficacy. Finally, our findings suggest that students' perceptions of professional programming practice may influence their expectations and negative self-assessments. By reducing the frequency that students self-assess negatively while programming, we may be able to improve self-efficacy and decrease dropout rates in CS.
KW - cs1
KW - persistence
KW - self-assessments
KW - self-efficacy
UR - http://www.scopus.com/inward/record.url?scp=85092182353&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092182353&partnerID=8YFLogxK
U2 - 10.1145/3372782.3406273
DO - 10.1145/3372782.3406273
M3 - Conference contribution
AN - SCOPUS:85092182353
T3 - ICER 2020 - Proceedings of the 2020 ACM Conference on International Computing Education Research
SP - 170
EP - 181
BT - ICER 2020 - Proceedings of the 2020 ACM Conference on International Computing Education Research
PB - Association for Computing Machinery
Y2 - 10 August 2020 through 12 August 2020
ER -