Modeling negative affect detector of novice programming students through keyboard dynamics and mouse behavior
Date of Award
Doctor of Philosophy in Computer Science
Information Systems & Computer Science
Rodrigo, Ma. Mercedes T., Ph.D.
This study developed affective models for detecting negative affective states, particularly boredom, confusion, and frustration, among novice programming students learning C++, using keyboard dynamics and mouse behaviors. It also discovered patterns that reflect the relationship of students affect with keystrokes and mouse features. These features were extracted from mouse-key logs gathered from 55 novice C++ students and were labeled with the affective states observed from the corresponding video logs. The keystroke dynamic features are already sufficient to model negative affect detector. However, adding mouse behavior, specifically the distance it travelled along the x-axis, slightly improved the models performance. The idle time and typing error are the most notable features that predominantly influence the detection of negative affect. The idle time has the greatest influence in detecting high and fair boredom, while typing error comes before the idle time for low boredom. Conversely, typing error has the highest influence in detecting high and fair confusion, while idle time comes before typing error for low confusion. Though typing error is also the primary indicator of high and fair frustrations, some other features are still needed before it is acknowledged as such. Lastly, there is a very slim chance to detect low frustration.
(2017). Modeling negative affect detector of novice programming students through keyboard dynamics and mouse behavior. Ateneo de Manila University.