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

Additional URL

https://doi.org/10.1109/ICCSE.2015.7250273

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