Computer-based writing systems have been developed to provide students with instruction and deliberate practice on their writing. While generally successful in providing accurate scores, a common criticism of these systems is their lack of personalization and adaptive instruction. In particular, these systems tend to place the strongest emphasis on delivering accurate scores, and therefore, tend to overlook additional variables that may ultimately contribute to students' success, such as their affective states during practice. This study takes an initial step toward addressing this gap by building a predictive model of students' affect using information that can potentially be collected by computer systems. We used individual difference measures, text features, and keystroke analyses to predict engagement and boredom in 132 writing sessions. The results from the current study suggest that these three categories of features can be utilized to develop models of students' affective states during writing sessions. Taken together, features related to students' academic abilities, text properties, and keystroke logs were able to more than double the accuracy of a classifier to predict affect. These results suggest that information readily available in compute-based writing systems can inform affect detectors and ultimately improve student models within intelligent tutoring systems.
: Laura Allen, Caitlin Mills, Matthew Jacovina, Scott Crossley, Sidney D'Mello and Danielle McNamara (2016). "Investigating Boredom and Engagement during Writing Using Multiple Sources of Information: The Essay, The Writer, and Keystrokes". In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK '16). ACM, New York.