Detecting boredom and engagement during writing with keystroke analysis, task appraisals, and stable traits

It is hypothesized that the ability for a system to automatically detect and respond to users' affective states can greatly enhance the human-computer interaction experience. Although there are currently many options for affect detection, keystroke analysis offers several attractive advantages to traditional methods. In this paper, we consider the possibility of automatically discriminating between natural occurrences of boredom, engagement, and neutral by analyzing keystrokes, task appraisals, and stable traits of 44 individuals engaged in a writing task. The analyses explored several different arrangements of the data: using downsampled and/or standardized data; distinguishing between three different affect states or groups of two; and using keystroke/timing features in isolation or coupled with stable traits and/or task appraisals. The results indicated that the use of raw data and the feature set that combined keystroke/timing features with task appraisals and stable traits, yielded accuracies that were 11% to 38% above random guessing and generalized to new individuals. Applications of our affect detector for intelligent interfaces that provide engagement support during writing are discussed.