This study aims at predicting affective states during programming using keyboard and mouse data. The article proposes and evaluates a novel set of features under programming context to predict affective states. Fourteen undergraduate participants performed three programming tasks of varying difficulties. At the completion of each task, participants reported their affective states by viewing webcam videos and screen recordings. Features extracted from keyboard and mouse logs were used to train multiple classifiers. Among trained classifiers, feedforward neural network recognized positive, neutral and negative states with 52.9% accuracy. The overall Cohen's Kappa reached 0.27. Without neutral states, the classifiers were able to differentiate positive and negative states with 74.1% accuracy and 0.48 Kappa. Our approach demonstrates improved ability of predicting self-labelled affective states of programmers from keyboard and mouse data, without using specialized sensors, and potential of emotional feedback to programmers during learning to deliver better experience.
展开▼
