An Evaluation of Mouse and Keyboard Interaction Indicators towards Non-intrusive and Low Cost Affective Modeling in an Educational Context

In this paper we propose a series of indicators, which derive from user's interactions with mouse and keyboard. The goal is to evaluate their use in identifying affective states and behavior changes in an e-learning platform by means of non-intrusive and low cost methods. The approach we have followed study user's interactions regardless of the task being performed and its presentation, aiming at finding a solution applicable in any domain. In particular, mouse movements and clicks, as well as keystrokes were recorded during a math problem solving activity where users involved in the experiment had not only to score their degree of valence (i.e., pleasure versus displeasure) and arousal (i.e., high activation versus low activation) of their affective states after each problem by using the Self-Assessment-Manikin scale, but also type a description of their own feelings. By using that affective labeling, we evaluated the information provided by these different indicators processed from the original user's interactions logs. In total, we computed 42 keyboard indicators and 96 mouse indicators.