CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Distance, dependencies, and delay in a global collaboration
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
How does radical collocation help a team succeed?
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Work rhythms: analyzing visualizations of awareness histories of distributed groups
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Mylar: a degree-of-interest model for IDEs
Proceedings of the 4th international conference on Aspect-oriented software development
Examining task engagement in sensor-based statistical models of human interruptibility
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Maintaining mental models: a study of developer work habits
Proceedings of the 28th international conference on Software engineering
Automatic prediction of frustration
International Journal of Human-Computer Studies
Novice software developers, all over again
ICER '08 Proceedings of the Fourth international Workshop on Computing Education Research
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Proceedings of the 16th ACM international conference on Supporting group work
An editing-operation replayer with highlights supporting investigation of program modifications
Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th annual ERCIM Workshop on Software Evolution
Computer Supported Cooperative Work
Hi-index | 0.00 |
It would be useful if software engineers/instructors could be aware that remote team members/students are having difficulty with their programming tasks. We have developed an approach that tries to automatically create this semantic awareness based on developers' interactions with the programming environment, which is extended to log these interactions and allow the developers to train or supervise the algorithm by explicitly indicating they are having difficulty. Based on the logs of six programmers, we have found that our approach has high accuracy.