All Outputs (4)

No Tests Required: Comparing Traditional and Dynamic Predictors of Programming Success (2014)
Presentation / Conference Contribution
Watson, C., Li, F. W., & Godwin, J. L. (2014). No Tests Required: Comparing Traditional and Dynamic Predictors of Programming Success. In J. . D. Dougherty, K. Nagel, A. Decker, & K. Eiselt (Eds.), Proceedings of the 45th ACM Technical Symposium on Computer Science Education (469-474). https://doi.org/10.1145/2538862.2538930

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... Read More about No Tests Required: Comparing Traditional and Dynamic Predictors of Programming Success.

Failure rates in introductory programming revisited (2014)
Presentation / Conference Contribution
Watson, C., & Li, F. W. (2014). Failure rates in introductory programming revisited. In Å. Cajander, M. Daniels, T. Clear, & A. Pears (Eds.), Proceedings of the 2014 conference on Innovation & technology in computer science education (ITiCSE '14) (39-44). https://doi.org/10.1145/2591708.2591749

Whilst working on an upcoming meta-analysis that synthesized fifty years of research on predictors of programming performance, we made an interesting discovery. Despite several studies citing a motivation for research as the high failure rates of int... Read More about Failure rates in introductory programming revisited.

Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior (2013)
Presentation / Conference Contribution
Watson, C., Li, F. W., & Godwin, J. L. (2013). Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior. In Proceedings of the 2013 IEEE 13th International Conference on Advanced Learning Technologies (ICALT 2013) (319-323). https://doi.org/10.1109/icalt.2013.99

The high failure rates of many programming courses means there is a need to identify struggling students as early as possible. Prior research has focused upon using a set of tests to assess the use of a student's demographic, psychological and cognit... Read More about Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior.

BlueFix: Using Crowd-sourced Feedback to Support Programming Students in Error Diagnosis and Repair (2012)
Presentation / Conference Contribution
Watson, C., Li, F. W., & Godwin, J. L. (2012). BlueFix: Using Crowd-sourced Feedback to Support Programming Students in Error Diagnosis and Repair. In E. Popescu, Q. Li, R. Klamma, H. Leung, & M. Specht (Eds.), Advances in Web-Based Learning - ICWL 2012: 11th International Conference, Sinaia, Romania, September 2-4, 2012 ; proceedings (228-239). https://doi.org/10.1007/978-3-642-33642-3_25

Feedback is regarded as one of the most important influences on student learning and motivation. But standard compiler feedback is designed for experts - not novice programming students, who can find it difficult to interpret and understand. In this... Read More about BlueFix: Using Crowd-sourced Feedback to Support Programming Students in Error Diagnosis and Repair.