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Manual Analysis of Homework Coding Errors for Improved Teaching and Help

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Technical Session 12: Teaching and Learning

Tagged Division

Computers in Education

Page Count

15

DOI

10.18260/1-2--33083

Permanent URL

https://peer.asee.org/33083

Download Count

430

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Paper Authors

biography

Nabeel Alzahrani University of California, Riverside

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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.

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biography

Frank Vahid University of California, Riverside

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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.

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biography

Alex Daniel Edgcomb zyBooks, A Wiley Brand

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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.

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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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