Automatic detection of frustration of novice programmers from contextual and keystroke logs
Publication Type
Conference Proceeding Article
Publication Date
7-2015
Abstract
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.
Keywords
keystrokes, frustration, novice, learning, programming
Discipline
Graphics and Human Computer Interfaces | Numerical Analysis and Scientific Computing | Programming Languages and Compilers
Research Areas
Information Systems and Management
Publication
2015 10th International Conference on Computer Science & Education (ICCSE): July 22-24, Cambridge: Proceedings
First Page
373
Last Page
377
ISBN
9781479966004
Identifier
10.1109/ICCSE.2015.7250273
Publisher
IEEE
City or Country
Pist
Citation
FWA, Hua Leong.
Automatic detection of frustration of novice programmers from contextual and keystroke logs. (2015). 2015 10th International Conference on Computer Science & Education (ICCSE): July 22-24, Cambridge: Proceedings. 373-377.
Available at: https://ink.library.smu.edu.sg/sis_research/6909
Additional URL
https://doi.org/10.1109/ICCSE.2015.7250273