Luc Paquette, Ryan S. J. D. Baker, Michael A. Sao Pedro, Janice D. Gobert, Lisa Rossi, Adam Nakama et al.
Recently, there has been considerable interest in understanding the relationship between student affect and cognition. This research is facilitated by the advent of automated sensor-free detectors that have been designed to “infer” affect from the logs of student interactions within a learning environment. Such detectors allow for fine-grained analysis of the impact of different affective states on a range of learning outcome measures. However, these detectors have to date only been developed for a subset of online learning environments, including problem-solving tutors, dialogue tutors, and narrative-based virtual environments. In this paper, we extend sensor-free affect detection to a science microworld environment, affording the possibility of more deeply studying and responding to student affect in this type of learning environment.
The final publication is available at Springer via https://doi.org/10.1007/978-3-319-07221-0_1.