Automatic detection of frustration of novice programmers from contextual and keystroke logs
Novice programmers exhibit a repertoire of affective states over time when they are learning computer programming. The modeling of frustration is important as it informs on the need for pedagogical intervention of the student who may otherwise lose confidence and interest in the learning. In this pa...
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sg-smu-ink.sis_research-79122022-02-07T02:36:02Z Automatic detection of frustration of novice programmers from contextual and keystroke logs FWA, Hua Leong Novice programmers exhibit a repertoire of affective states over time when they are learning computer programming. The modeling of frustration is important as it informs on the need for pedagogical intervention of the student who may otherwise lose confidence and interest in the learning. In this paper, contextual and keystroke features of the students within a Java tutoring system are used to detect frustration of student within a programming exercise session. As compared to psychological sensors used in other studies, the use of contextual and keystroke logs are less obtrusive and the equipment used (keyboard) is ubiquitous in most learning environment. The technique of logistic regression with lasso regularization is utilized for the modeling to prevent over-fitting. The results showed that a model that uses only contextual and keystroke features achieved a prediction accuracy level of 0.67 and a recall measure of 0.833. Thus, we conclude that it is possible to detect frustration of a student from distilling both the contextual and keystroke logs within the tutoring system with an adequate level of accuracy. 2015-07-24T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/6909 info:doi/10.1109/ICCSE.2015.7250273 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University keystrokes frustration novice learning programming Graphics and Human Computer Interfaces Numerical Analysis and Scientific Computing Programming Languages and Compilers |
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keystrokes frustration novice learning programming Graphics and Human Computer Interfaces Numerical Analysis and Scientific Computing Programming Languages and Compilers FWA, Hua Leong Automatic detection of frustration of novice programmers from contextual and keystroke logs |
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Novice programmers exhibit a repertoire of affective states over time when they are learning computer programming. The modeling of frustration is important as it informs on the need for pedagogical intervention of the student who may otherwise lose confidence and interest in the learning. In this paper, contextual and keystroke features of the students within a Java tutoring system are used to detect frustration of student within a programming exercise session. As compared to psychological sensors used in other studies, the use of contextual and keystroke logs are less obtrusive and the equipment used (keyboard) is ubiquitous in most learning environment. The technique of logistic regression with lasso regularization is utilized for the modeling to prevent over-fitting. The results showed that a model that uses only contextual and keystroke features achieved a prediction accuracy level of 0.67 and a recall measure of 0.833. Thus, we conclude that it is possible to detect frustration of a student from distilling both the contextual and keystroke logs within the tutoring system with an adequate level of accuracy. |
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FWA, Hua Leong |
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FWA, Hua Leong |
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FWA, Hua Leong |
title |
Automatic detection of frustration of novice programmers from contextual and keystroke logs |
title_short |
Automatic detection of frustration of novice programmers from contextual and keystroke logs |
title_full |
Automatic detection of frustration of novice programmers from contextual and keystroke logs |
title_fullStr |
Automatic detection of frustration of novice programmers from contextual and keystroke logs |
title_full_unstemmed |
Automatic detection of frustration of novice programmers from contextual and keystroke logs |
title_sort |
automatic detection of frustration of novice programmers from contextual and keystroke logs |
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Institutional Knowledge at Singapore Management University |
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2015 |
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https://ink.library.smu.edu.sg/sis_research/6909 |
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