Emily S. Tabanao
ABSTRACT
In this study, we attempted to quantify indicators of novice programmer progress in the task of writing programs, and we evaluated the use of these indicators for identifying academically at-risk students. Over the course of nine weeks, students completed five different graded programming exercises in a computer lab. Using an instrumented version of BlueJ, an integrated development environment for Java, we collected novice compilations and explored the errors novices encountered, the locations of these errors, and the frequency with which novices compiled their programs. We identified which frequently encountered errors and which compilation behaviors were characteristic of at-risk students. Based on these findings, we developed linear regression models that allowed prediction of students' scores on a midterm exam. However, the models derived could not accurately predict the at-risk students. Although our goal of identifying at-risk students was not attained, we have gained insights regarding the compilation behavior of our students, which may help us identify students who are in need of intervention.
- Ahmadzadeh, M., Elliman, D. and Higgins, C. An analysis of patterns of debugging among novice computer science students. In Proceedings of the 10th annual SIGCSE conference on Innovation and technology in computer science education (ITiCSE '05). ACM, New York, NY, USA, 84--88. http://doi.acm.org/10.1145/1067445.1067472 Google Scholar
Digital Library
- Al-Barakati, N., Al-Aama, A., 2009. The effect of visualizing roles of variables on student performance in an introductory programming course. SIGCSE Bull. 41, 3 (July 2009), 228--232. http://doi.acm.org/10.1145/1595496.1562949 Google ScholarDigital Library
- Bennedsen, J., Caspersen M. E. 2008. Optimists Have More Fun, But Do They Learn Better? - On the Influence of Emotional and Social Factors on Learning Introductory Computer Science. Computer Science Education, 18, 1, 1--16.Google ScholarCross Ref
- Ben-Ari, M. 1998. Constructivism in computer science education. ACM Press New York, NY, USA. Vol. 30(1). Google ScholarDigital Library
- Bergin, S., Reilly, R.: Programming: Factors that influence success. SIGCSE 2005. Proceedings of the thirty-fifth SIGCSE technical symposium on Computer Science Education. St. Louis, Illinois, US. February 2005, 411--415. Google ScholarDigital Library
- Bornat, R., Dehnadi, S., Simon. 2008. Mental models, consistency and programming aptitude. In Proceedings of the tenth conference on Australasian computing education - Volume 78 (ACE '08), Simon Hamilton and Margaret Hamilton (Eds.), Vol. 78. Australian Computer Society, Inc., Darlinghurst, Australia, Australia, 53--61. Google ScholarDigital Library
- Byckling, P., Sajaniemi, J., 2006. Roles of variables and programming skills improvement. SIGCSE Bull. 38, 1 (March 2006), 413--417. DOI=10.1145/1124706.1121470 http://doi.acm.org/10.1145/1124706.1121470 Google ScholarDigital Library
- Dehnadi, Saeed. A Cognitive Study of Learning to Program in Introductory Programming Courses {Doctoral Thesis}. Retrieved from http://eprints.mdx.ac.uk/6274/1/Dehnadi_A_Cognitive_Study_of_Learning.pdfGoogle Scholar
- duBoulay, B. 1986. Some difficulties of learning to program. Journal of Educational Computing Research, Vol. 2, pp. 57--73.Google ScholarCross Ref
- Fenwick, J. B., Jr., Norris, C., Barry, F. E., Rountree, J., Spicer, C. J. and Cheek, S. D. 2009. Another look at the behaviors of novice programmers. SIGCSE Bull. 41, 1 (March 2009), 296--300. DOI=10.1145/1539024.1508973 http://doi.acm.org/10.1145/1539024.1508973 Google ScholarDigital Library
- Goold, A. and Rimmer, R. (2000): Factors affecting performance in first-year computing. ACM SIGCSE Bulletin, 32(2): 39--43. Google ScholarDigital Library
- Hagan, D., Markham, S., 2000. Does it help to have some programming experience before beginning a computing degree program? SIGCSE Bull. 32, 3 (July 2000), 25--28. http://doi.acm.org/10.1145/353519.343063 Google ScholarDigital Library
- Jadud, M. C. (2005). A first look at novice compilation behavior using BlueJ. Computer Science Education, 15(1), 25--40.Google ScholarCross Ref
- Jadud, M. C., 2006. Methods and tools for exploring novice compilation behaviour. Proceedings of the 2006 international workshop on Computing education research. New York, NY, USA : ACM Press. pp. 73--84. Google ScholarDigital Library
- Khan, I., Hierons, M., Brinkman, W. 2007. Mood independent programming. In Proceedings of the 14th European conference on Cognitive ergonomics: invent! explore! (ECCE '07). ACM, New York, NY, USA, 269--272. http://doi.acm.org/10.1145/1362550.1362606 Google ScholarDigital Library
- Kinnunen, P., Simon, B. 2010. Experiencing programming assignments in CS1: the emotional toll. In Proceedings of the Sixth international workshop on Computing education research (ICER '10). ACM, New York, NY, USA, 77--86. http://doi.acm.org/10.1145/1839594.1839609 Google ScholarDigital Library
- Lahtinen, E., Ala-Mutka, K. and Jarvinen, H.M. 2005. A study of the difficulties of novice programmers. ACM Press. ACM SIGCSE Bulletin. New York, NY, USA. Vol. 37(3), pp. 14--18. Google ScholarDigital Library
- McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Kolikant, Y. B., Laxer, C., Thomas, L., Utting, I., and Wilusz, T. 2001. A multi-national, multi-institutional study of assessment of programming skills of first-year CS students. In Working group reports from ITiCSE on Innovation and technology in computer science education (ITiCSE-WGR '01). ACM, New York, NY, USA, 125--180. http://doi.acm.org/10.1145/572133.572137 Google ScholarDigital Library
- Perkins, D. N., Hancock, C., Hobbs, R., Martin, F. 1986. Conditions of Learning in Novice Programmers. Journal of Educational Computing Research, N. 1, Vol. 2, pp. p37--55.Google Scholar
- Raftery, A. E.. Bayesian model selection in social research. Sociological Methodology, 25, 111--163, 2003.Google Scholar
- Robins, A., Rountree, J. & Rountree, N. 2003. Learning and Teaching Programming: A Review and Discussion. Taylor & Francis, Computer Science Education,13(2), pp. 137--172.Google ScholarCross Ref
- Rodrigo, M. M. T., Baker, R. 2009. Coarse-grained detection of student frustration in an introductory programming course. In Proceedings of the fifth international workshop on Computing education research workshop (ICER '09). ACM, New York, NY, USA, 75--80. http://doi.acm.org/10.1145/1584322.1584332 Google ScholarDigital Library
- Rodrigo, M. M. T., Baker R. S. J. d., Jadud, M. C., Amarra, A. M., Dy, T., Lahoz, M. B. E., Lim, S. 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 (July 2009), 156--160. http://doi.acm.org/10.1145/1595496.1562929 Google ScholarDigital Library
- Wilson, B. (2002): A study of factors promoting success in computer science including gender differences. Computer Science Education, 12(1-2):141--164.Google ScholarCross Ref
- Winslow, L. E. 1996. Programming pedagogy - A psychological overview. SIGCSE Bulletin, 28(3), pp. 17--22. Google ScholarDigital Library
Index Terms
- Predicting at-risk novice Java programmers through the analysis of online protocols
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