A Discipline for Software Engineering
A Discipline for Software Engineering
The effects of pair-programming on performance in an introductory programming course
SIGCSE '02 Proceedings of the 33rd SIGCSE technical symposium on Computer science education
Proceedings of the 25th International Conference on Software Engineering
Off-task behavior in the cognitive tutor classroom: when students "game the system"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Program quality with pair programming in CS1
Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education
Another look at the behaviors of novice programmers
Proceedings of the 40th ACM technical symposium on Computer science education
Affective and behavioral predictors of novice programmer achievement
ITiCSE '09 Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education
Comparing effective and ineffective behaviors of student programmers
ICER '09 Proceedings of the fifth international workshop on Computing education research workshop
Tatiana: an environment to support the CSCL analysis process
CSCL'09 Proceedings of the 9th international conference on Computer supported collaborative learning - Volume 1
CSCL'09 Proceedings of the 9th international conference on Computer supported collaborative learning - Volume 1
The benefits of pairing by ability
Proceedings of the 41st ACM technical symposium on Computer science education
Pair debugging: a transactive discourse analysis
Proceedings of the Sixth international workshop on Computing education research
Do values grow on trees?: expression integrity in functional programming
Proceedings of the seventh international workshop on Computing education research
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We present the preliminary analysis of a study whose long term aim is to track IDE usage to identify novice-programmers in need of support. Our analysis focused on the activity of 24 dyads on a 3 week assignment. We correlated frequencies of events such as use of code generation and of the debugger with assignment grades, final exam grades, and the difference in rankings within dyad on the final exam. Our results show several significant correlations. In particular, code generation and debugging are correlated with the final grade, and running in non-debug mode is correlated with differences in ranking. These results are encouraging as they show that it is possible to predict learning outcomes with simple frequency data and suggest more complex indicators could achieve robust prediction.