Jaime Spacco
Davide Fossati
ABSTRACT
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.
- A. Allevato, M. Thornton, S. H. Edwards, and M. A. Pérez-Quiñones. Mining data from an automated grading and testing system by adding rich reporting capabilities. In R. S. J. de Baker, T. Barnes, and J. E. Beck, editors, EDM, pages 167--176. www.educationaldatamining.org, 2008.Google Scholar
- T. Beaubouef and J. Mason. Why the high attrition rate for computer science students: some thoughts and observations. SIGCSE Bull., 37(2):103--106, June 2005. Google ScholarDigital Library
- P. Blikstein. Using learning analytics to assess students' behavior in open-ended programming tasks. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge, LAK '11, pages 110--116, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- N. S. Board. Science and engineering indicators digest 2012. 2012.Google Scholar
- L. Carter. Why students with an apparent aptitude for computer science don't choose to major in computer science. SIGCSE Bull., 38(1):27--31, Mar. 2006. Google ScholarDigital Library
- S. H. Edwards. Using software testing to move students from trial-and-error to reflection-in-action. SIGCSE Bull., 36(1):26--30, Mar. 2004. Google ScholarDigital Library
- S. H. Edwards, J. Snyder, M. A. Pérez-Quinones, A. Allevato, D. Kim, and B. Tretola. Comparing effective and ineffective behaviors of student programmers. In Proceedings of the fifth international workshop on Computing education research workshop, ICER '09, pages 3--14, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- N. Falkner and K. Falkner. A fast measure for identifying at-risk students in computer science. In Proceedings of the ninth annual international conference on International computing education research, pages 55--62. ACM, 2012. Google ScholarDigital Library
- J. B. Fenwick, Jr., C. Norris, F. E. Barry, J. Rountree, C. J. Spicer, and S. D. Cheek. Another look at the behaviors of novice programmers. In Proceedings of the 40th ACM technical symposium on Computer science education, SIGCSE '09, pages 296--300, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- M. C. Jadud. A first look at novice compilation behaviour using bluej. Computer Science Education, 15:1--25, 2005.Google ScholarCross Ref
- J. Kasurinen and U. Nikula. Estimating programming knowledge with bayesian knowledge tracing. In Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education, ITiCSE '09, pages 313--317, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- M. McCracken, V. Almstrum, D. Diaz, M. Guzdial, D. Hagan, Y. B.-D. Kolikant, C. Laxer, L. Thomas, I. Utting, and T. Wilusz. A multi-national, multi-institutional study of assessment of programming skills of first-year cs students. SIGCSE Bull., 33(4):125--180, Dec. 2001. Google ScholarDigital Library
- C. Murphy, G. Kaiser, K. Loveland, and S. Hasan. Retina: helping students and instructors based on observed programming activities. In Proceedings of the 40th ACM technical symposium on Computer science education, SIGCSE '09, pages 178--182, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- P. C. of Advisors on Science and Technology. Designing a digital future: Federally funded research and development in networking and information technology. 2013.Google Scholar
- N. A. of Colleges and Employers. Nace january 2013 salary survey. 2013.Google Scholar
- J. Spacco, J. Strecker, D. Hovemeyer, and W. Pugh. Software repository mining with marmoset: an automated programming project snapshot and testing system. In Proceedings of the 2005 international workshop on Mining software repositories, MSR '05, pages 1--5, New York, NY, USA, 2005. ACM. Google ScholarDigital Library
- E. S. Tabanao, M. M. T. Rodrigo, and M. C. Jadud. Predicting at-risk novice java programmers through the analysis of online protocols. In Proceedings of the seventh international workshop on Computing education research, ICER '11, pages 85--92, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- I. Utting, N. Brown, M. Kölling, D. McCall, and P. Stevens. Web-scale data gathering with bluej. In Proceedings of the ninth annual international conference on International computing education research, ICER '12, pages 1--4, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
Index Terms
- Towards improving programming habits to create better computer science course outcomes
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