Abstract
AbstractHigh attrition and dropout rates are common in introductory programming courses. One of the reasons students drop out is loss of motivation due to the lack of feedback and proper assessment of their progress. Hence, a process-oriented approach is needed in assessing programming progress, which entails examining and measuring students’ compilation behaviors and source codes. This paper reviews the elements of a process-oriented approach including previous studies that have used this approach. Specific metrics covered are Jadud’s Error Quotient, the Watwin Score, Probabilistic Distance to Solution, Normalized Programming State Model, and the Repeated Error Density.
Publisher
Springer Science and Business Media LLC
Subject
Management of Technology and Innovation,Media Technology,Education,Social Psychology
Reference80 articles.
1. Adu-Manusarpong, K., Arthur, J.K., Amoako, P.Y.O. (2013). Causes of failure of students in computer programming courses: The teacher learner perspective. International Journal of Computer Applications, 7(12), 27–32.
2. Ahadi, A., Lister, R., Haapala, H., Vihavainen, A. (2015). Exploring machine learning methods to automatically identify students in need of assistance. In Proceedings of the Eleventh Annual International Conference on International Computing Education Research. ACM, New York, (pp. 121–130).
3. Ahmadzadeh, M., Elliman, D., Higgins, C. (2005). An analysis of patterns of debugging among novice computer science students. SIGCSE Bull, 37(3), 84–88.
4. Allevato, A., & Edwards, S.H. (2010). Discovering patterns in student activity on programming assignments. In ASEE Southeastern Section Annual Conference and Meeting.
5. Allevato, A., Thornton, M., Edwards, S., Perez-Quinones, M. (2008). Mining data from an automated grading and testing system by adding rich reporting capabilities. Educational Data Mining.
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