Two full papers accepted to SIGCSE 2019

We’ve had two submissions accepted to SIGCSE 2019! The papers are:

  • “From Clusters to Content: Using Code Clustering for Course Improvement”, by myself, Ryan Arrison, Mehnaz Ruksana, Evi Salguero, Zida Wang, Ben Wellington, and Kevin Yin. This paper looks at using code clustering (automated grouping of students’ code submissions) to inform curricular revisions.
  • “Collaboration versus Cheating: Reducing Code Plagiarism in an Online MS Computer Science Program”, by Tony Mason, Ada Gavrilovska, and myself. This paper looks at detecting and deterring cheating in programming assignments.

To see the abstracts for these papers, keep reading after the jump.

Abstract (From Clusters to Content): Large undergraduate CS courses receive thousands of code submissions per term. To help make sense of the large quantities of submissions, projects have emerged to dynamically cluster student submissions by approach for writing scalable feedback, tailoring hints, and conducting research. However, relatively little attention has been paid to the value of these tools for informing revision to core course materials and delivery methods. In this work, we applied one such technology—Sense, the eponymous product of its company—to an online CS1 class delivered simultaneously for credit to on-campus students and for free to MOOC students. Using Sense, we clustered student submissions to around 70 problems used throughout the course. In this work, we discuss the value of such clustering, the surprising trends we discovered through this process, and the changes made or planned to the course based on the results. We also discuss broader ideas on injecting clustering results into course design.

Abstract (Collaboration versus Cheating):  We outline how we detected programming plagiarism in an introductory online course for a master’s of science in computer science program, how we achieved a statistically significant reduction in programming plagiarism by combining a clear explanation of university and class policy on academic honesty reinforced with a short but formal assessment, and how we evaluated plagiarism rates before and after implementing our policy and assessment.

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