Learning with Style: Improving Student Code-Style Through Better Automated FeedbackGlobal
This work introduces and evaluates ccheck, a lenient automatic grader and C style-checker, to guide students to improve their coding practices. Many computing classes rely heavily on autograders–software that automates grading and alleviates staff workload in classes with large enrollments. At best, autograders offer timely and consistent feedback to students. However, existing autograders primarily judge on functional correctness—they are generally strict and inflexible in marking beginner programming assignments. They tend not to provide feedback on programming style and structure, which instead requires delayed, tedious manual assessment. ccheck, the tool we introduce, aims to address this gap and provide more meaningful, real-time feedback with a pedagogical focus.
We deploy and evaluate ccheck in a class of 440 first-year computing students. Tutors employ the system for marking assistance, while students use the same system for self-evaluation prior to finalizing their submissions. Feedback was solicited through a survey of 76 students and a focus group of the teaching team. 82% of the students surveyed said that the system helped them learn good coding practices, while 75% emphasized that the feedback received from the system is meaningful and helpful. The teaching team focus group related to how they valued the automation of menial marking tasks, which enabled them to direct their time toward other meaningful feedback. Overall, we find that teaching, learning and student experiences are improved through the deployment of ccheck.
Fri 22 MarDisplayed time zone: Pacific Time (US & Canada) change
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15:45 25mTalk | Advancing Automated Assessment Tools — Opportunities for Innovations in Upper-level Computing Courses: A Position PaperGlobal Papers Steffan Hooper University of Auckland, Burkhard Wünsche University of Auckland, Andrew Luxton-Reilly The University of Auckland, Paul Denny The University of Auckland, Haoran Feng University of Auckland DOI | ||
16:10 25mTalk | Generating Multi-Part Autogradable Faded Parsons Problems From Code-Writing Exercises Papers Serena Caraco University of California, Berkeley, Nelson Lojo University of California, Berkeley, Michael Verdicchio The Citadel, Armando Fox UC Berkeley DOI | ||
16:35 25mTalk | Learning with Style: Improving Student Code-Style Through Better Automated FeedbackGlobal Papers Liam Saliba The University of Melbourne, Elisa Shioji The University of Melbourne, Eduardo Araujo Oliveira The University of Melbourne, Shaanan Cohney University of Melbourne, Jianzhong Qi The University of Melbourne DOI |