Manual Analysis of Homework Coding Errors for Improved Teaching and Help
- Conference
- Location
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Tampa, Florida
- Publication Date
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June 15, 2019
- Start Date
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June 15, 2019
- End Date
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June 19, 2019
- Conference Session
- Tagged Division
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Computers in Education
- Page Count
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15
- DOI
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10.18260/1-2--33083
- Permanent URL
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https://peer.asee.org/33083
- Download Count
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857
Paper Authors
Nabeel Alzahrani University of California, Riverside
Nabeel Alzahrani is a Computer Science Ph.D. student in the Department of Computer Science and Engineering at the University of California, Riverside. Nabeel's research interests include causes of student struggle, and debugging methodologies, in introductory computer programming courses.
Frank Vahid University of California, Riverside
Frank Vahid is a Professor of Computer Science and Engineering at the Univ. of California, Riverside. His research interests include embedded systems design, and engineering education. He is a co-founder of zyBooks.com.
Alex Daniel Edgcomb zyBooks, A Wiley Brand
Alex Edgcomb is Sr. Software Engineer at zyBooks, a startup spun-off from UC Riverside and acquired by Wiley. zyBooks develops interactive, web-native learning materials for STEM courses. Alex actively studies and publishes the efficacy of web-native learning materials on student outcomes.
Abstract
Previous research reports common student errors in introductory programming (CS1) classes. Knowing common errors enables us to improve teaching and content to train students to avoid those errors, and to provide an automated help system like providing hints based on particular auto-detected errors. Finding and fixing some errors is a part of learning, so our focus is specifically on errors that cause struggle, meaning excessive time or attempts. Struggle may lead to giving up, loss of confidence, or cheating. For 89 online auto-graded coding homework problems in our CS1 class of 100 students (mostly engineering/science majors), we first automatically determined the 12 problems with the highest struggle rates. Then, we spent about 100 hours manually examining incorrect student submissions to determine what errors caused struggle and the time spent on each error. Like previous work, we found many common general errors, like using = rather than ==. However, we also found problem-specific errors, like misusing a particular library function, leading to a first conclusion that a help system should allow teachers/authors to add problem-specific hints. Furthermore, we analyzed errors that caused the longest struggle, and found some uncommon "one-off" errors, leading to a second conclusion that a help system won't be able to detect all errors and thus might need automated recommending or alerting for human assistance (or other techniques).
Alzahrani, N., & Vahid, F., & Edgcomb, A. D. (2019, June), Manual Analysis of Homework Coding Errors for Improved Teaching and Help Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--33083
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