ABSTRACT
Research over the past fifty years into predictors of programming performance has yielded little improvement in the identification of at-risk students. This is possibly because research to date is based upon using static tests, which fail to reflect changes in a student's learning progress over time. In this paper, the effectiveness of 38 traditional predictors of programming performance are compared to 12 new data-driven predictors, that are based upon analyzing directly logged data, describing the programming behavior of students. Whilst few strong correlations were found between the traditional predictors and performance, an abundance of strong significant correlations based upon programming behavior were found. A model based upon two of these metrics (Watwin score and percentage of lab time spent resolving errors) could explain 56.3% of the variance in coursework results. The implication of this study is that a student's programming behavior is one of the strongest indicators of their performance, and future work should continue to explore such predictors in different teaching contexts.
- Bennedsen, J. and Caspersen, M.E. 2007. Failure rates in introductory programming. SIGCSE Bull. 39(2), 32--36. Google ScholarDigital Library
- Bergin, S. and Reilly, R. 2005. Programming: factors that influence success. SIGCSE Bull. 37(1), 411--415. Google ScholarDigital Library
- Bergin, S., Reilly, R. and Traynor, D. 2005. Examining the role of self-regulated learning on introductory programming performance. In Proc. of the 1st Int. Workshop on Computing Education Research (ICER), 81--86. Google ScholarDigital Library
- Bergin, S. and Reilly, R. 2005. The influence of motivation and comfort-level on learning to program. In Proc. of the 17th PPIG Workshop, 293--304.Google Scholar
- Biamonte, A.J. 1964. Predicting success in programmer training. In Proc. of the 2nd SIGCPR Conference on Computer Personnel Research, 9--12. Google ScholarDigital Library
- Byrne, P. and Lyons, G. 2001. The effect of student attributes on success in programming. SIGCSE Bull. 33(3), 49--52. Google ScholarDigital Library
- Campbell, V. and Johnstone, M. 2010. The Significance of Learning Style with Respect to Achievement in First Year Programming Students. In Proc. of the 21st Australian Software Engineering Conference (ASWEC), 165--170. Google ScholarDigital Library
- Cantwell-Wilson, B. and Shrock, S. 2001. Contributing to success in an introductory computer science course: a study of twelve factors. SIGCSE Bull. 33(1), 184--188. Google ScholarDigital Library
- Chamillard A.T. and Karolick, D. 1999. Using learning style data in an introductory computer science course. SIGCSE Bull. 31(1), 291--295. Google ScholarDigital Library
- Corman, L.S. 1986. Cognitive style, personality type, and learning ability as factors in predicting the success of the beginning programming student. SIGCSE Bull. 18(4), 80--89. Google ScholarDigital Library
- Henry, J.W., Martinko, M.J., and Pierce, M.A. 1994. Attributional style as a predictor of success in a first computer science course. Computers in Human Behavior, 9(4), 341--352.Google ScholarCross Ref
- Jadud, M.C. 2006. Methods and tools for exploring novice compilation behaviour. Proc. of the 2nd Int. Workshop on Computing Education Research (ICER), 73--84. Google ScholarDigital Library
- Lau, W.W., and Yuen, A.H. 2009. Exploring the effects of gender and learning styles on computer programming performance implications for programming pedagogy. British Journal of Educational Technology, 40(4), 696--712.Google ScholarCross Ref
- Lau, W.W., and Yuen, A.H. 2011. Modelling programming performance: Beyond the influence of learner characteristics. Computers & Education, 57(1), 1202--1213. Google ScholarDigital Library
- Pintrich, P. R., Smith, D. A., García, T., & McKeachie, W. J. 1993. Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and psychological measurement, 53(3), 801--813.Google Scholar
- Rodrigo, M.M.T., Baker, R.S., Jadud, M.C., Amarra, A.C.M., Dy, T., Espejo-Lahoz, M.B.V, Lim, S.A.L., Pascua, S.A.M.S., Sugay, J.O., and Tabanao, E.S. 2009. Affective and behavioral predictors of novice programmer achievement. SIGCSE Bull. 41(3), 156--160. Google ScholarDigital Library
- Stein, M.V. 2002. Mathematical preparation as a basis for success in CS-II. Computing Sciences in Colleges, 17(4), 28--38. Google ScholarDigital Library
- Ventura, P. 2005. Identifying predictors of success for an objects-first CS1. Computer Science Education, 15(3), 223--243.Google ScholarCross Ref
- Watson, C., Li, F.W.B., and Lau, R.W.H. 2010. A pedagogical interface for authoring adaptive e-learning courses. In Proc. of the 2nd Int. Workshop on Multimedia Technologies for Distance Learning, 13--18. Google ScholarDigital Library
- Watson, C., Li, F.W.B., and Godwin, J.L. 2012. BlueFix: using crowd sourced feedback to support programming students in error diagnosis and repair. In Proc. of 11th Int. Conference on Advances in Web-Based Learning, 228--239. Google ScholarDigital Library
- Li, F.W.B., and Watson, C. 2011. Game-based concept visualization for learning programming. In Proc. of the 3rd Int. Workshop on Multimedia Technologies for Distance Learning (MTDL), 37--42. Google ScholarDigital Library
- Watson, C., Li, F.W.B., and Lau, R.W.H. 2011. Learning programming languages through corrective feedback and concept visualisation. In Proc. of 10th Int. Conference on Advances in Web-Based Learning (ICWL), 11--20. Google ScholarDigital Library
- Watson, C., Li, F.W.B., and Godwin, J.L. 2013. Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior. In Proc. of the 13th IEEE International Conference on Advanced Learning Technologies (ICALT), 319--323. Google ScholarDigital Library
- White, G., and Sivitanides, M. 2003. An empirical investigation of the relationship between success in mathematics and visual programming courses. Information Systems Education, 14(4), 409--416.Google Scholar
- Wiedenbeck, S. 2005. Factors affecting the success of non-majors in learning to program. In Proc. of the 1st Int. Workshop on Computing Education Research (ICER), 13--24. Google ScholarDigital Library
Index Terms
- No tests required: comparing traditional and dynamic predictors of programming success
Recommendations
Mental models and programming aptitude
Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education (ITiCSE'07)Predicting the success of students participating in introductory programming courses has been an active research area for more than 25 years. Until recently, no variables or tests have had any significant predictive power. However, Dehnadi and Bornat ...
Wanted: CS1 students. no experience required
This paper reports research on the effect of prior programming experience on success in an objects-first CS1. In an objects-first, approach students are taught from the very beginning to think in terms of objects and the fundamentals of object-oriented ...
Mental models and programming aptitude
ITiCSE '07: Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science educationPredicting the success of students participating in introductory programming courses has been an active research area for more than 25 years. Until recently, no variables or tests have had any significant predictive power. However, Dehnadi and Bornat ...
Comments