Preface to Special Issue on User Modelling to Support Personalization in Enhanced Educational Settings
Automated detection of constructs associated with student engagement, disengagement, and meta-cognition plays an increasingly prominent part of personalized online education. Often these detectors are trained with ground truth labels obtained from field observations, a method that balances collection speed with label quality. Some behaviors and affective states (e.g., boredom) are regularly modeled across learning environments, but other constructs (e.g., gaming the system) manifest in fewer systems. New environments create the possibility of entirely unexpected constructs. In this paper, we describe how a field observation protocol (already proven effective for affect and behavior detection in several systems) was adapted to provide the flexibility needed to document previously unidentified or rare constructs. Specifically, we describe the in-field modification of the Baker Rodrigo Ocumpaugh Monitoring Protocol (BROMP) to accommodate categories not previously established (e.g., creative metanarrative) during observations of an educational multi-user virtual environment (MUVE). We also discuss the importance of developing methods that allow researchers to conduct such explorations while still capturing standard data constructs.