Using software testing to move students from trial-and-error to reflection-in-action
Proceedings of the 35th SIGCSE technical symposium on Computer science education
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Proceedings of the 37th SIGCSE technical symposium on Computer science education
Retina: helping students and instructors based on observed programming activities
Proceedings of the 40th ACM technical symposium on Computer science education
Another look at the behaviors of novice programmers
Proceedings of the 40th ACM technical symposium on Computer science education
Estimating programming knowledge with Bayesian knowledge tracing
ITiCSE '09 Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education
Comparing effective and ineffective behaviors of student programmers
ICER '09 Proceedings of the fifth international workshop on Computing education research workshop
Predicting at-risk novice Java programmers through the analysis of online protocols
Proceedings of the seventh international workshop on Computing education research
Using learning analytics to assess students' behavior in open-ended programming tasks
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
Web-scale data gathering with BlueJ
Proceedings of the ninth annual international conference on International computing education research
A fast measure for identifying at-risk students in computer science
Proceedings of the ninth annual international conference on International computing education research
Recording and analyzing in-browser programming sessions
Proceedings of the 13th Koli Calling International Conference on Computing Education Research
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We examine a large dataset collected by the Marmoset system in a CS2 course. The dataset gives us a richly detailed portrait of student behavior because it combines automatically collected program snapshots with unit tests that can evaluate the correctness of all snapshots. We find that students who start earlier tend to earn better scores, which is consistent with the findings of other researchers. We also detail the overall work habits exhibited by students. Finally, we evaluate how students use release tokens, a novel mechanism that provides feedback to students without giving away the code for the test cases used for grading, and gives students an incentive to start coding earlier. We find that students seem to use their tokens quite effectively to acquire feedback and improve their project score, though we do not find much evidence suggesting that students start coding particularly early.