Cited by 31Academic integrity and plagiarismSoftware Engineering ResearchOnline Learning and Analytics

TMOSS: Using Intermediate Assignment Work to Understand Excessive Collaboration in Large Classes

Yan, Lisa, McKeown, Nick, Sahami, Mehran, Piech, Chris·2018·Proceedings of the 49th ACM Technical Symposium on Computer Science Education

Abstract

As computer science classes grow, instructor workload also increases: teachers must simultaneously teach material, provide assignment feedback, and monitor student progress. At scale, it is hard to know which students need extra help, and as a result some students can resort to excessive collaboration--using online resources or peer code--to complete their work. In this paper, we present TMOSS, a tool that analyzes the intermediate steps a student takes to complete a programming assignment. We find that for three separate course offerings, TMOSS is almost twice as effective as traditional software similarity detectors in identifying the number of students who exhibit excessive collaboration. We also find that such students spend significantly less time on their assignment, use fewer class tutoring resources, and perform worse on exams than their peers. Finally, we provide a theory of the parametric distribution of typical student assignment similarity, which allows for probabilistic interpretation.

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Crossref · 10.1145/3159450.3159490

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19 of 19 references verified0 citation errors · 0 not checkable

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Authors
Yan, L., McKeown, N., Sahami, M., & Piech, C.
Affiliations
Stanford University (US)
Venue
Proceedings of the 49th ACM Technical Symposium on Computer Science Education
Publisher
ACM
Published
2018-02-21
Pages
110-115
OpenAlex
W2789176835
Cited by
30
References
19
Citations by year
2025: 3 · 2024: 6 · 2022: 4 · 2021: 4 · 2020: 3 · 2019: 7 · 2018: 3
FWCI
7.60
Type
conference paper
Language
en
Open access
closed
Locations
ACM Technical Symposium on Computer Science Education (SIGCSE) (publishedVersion)
Funders
National Science Foundation (http://dx.doi.org/10.13039/100000001) (DGE-114747)
Topics
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
online resources, programming exercises, student performance, instructor workload, programming education, parametric distributions

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