Emotions play a central role in shaping learning within digital environments. Although their effects may depend on how students' emotional experiences manifest into concrete behaviors, the links between these dimensions remain underexplored. This study investigates the most common behaviors during episodes of boredom, confusion, frustration, and engaged concentration in an educational game, as well as associations with situational interest, self-efficacy, prior knowledge, and learning gains, using interaction logs and sensor-free affect detectors. Results show that boredom is linked to off-task roaming, both consistently associated with lower motivation and learning. In contrast, behaviors during engaged concentration, frustration, and especially confusion vary widely, shaped by motivational traits and prior knowledge and offering diverse associations with learning. Concrete regulatory responses in these states—such as systematizing findings with in-game tools, skimming domain content to resolve doubts, or testing hypotheses—are positively associated with learning and motivation, reflecting students' ability to regulate emotions and address cognitive challenges. However, less constructive responses, such as aimless wandering, were tied to lower knowledge and motivation, underscoring the need for additional support. These findings extend existing affective theory by underscoring the importance of considering the behavioral dimension when analyzing students' emotions in digital learning environments.

Recent research seeks to develop more comprehensive learner models for adaptive learning software. For example, models of reading comprehension built using data from students' use of adaptive instructional software for mathematics have recently been developed. These models aim to deliver experiences that consider factors related to learning beyond performance in the target domain for instruction. We investigate the extent to which generalization is possible for a recently developed predictive model that seeks to infer students' reading comprehension ability (as measured by end-of-year standardized test scores) using an introductory learning experience in Carnegie Learning's MATHia intelligent tutoring system for mathematics. Building on a model learned on data from middle school students in a single school district in a mid-western U.S. state, using that state's end-of-year English Language Arts (ELA) standardized test score as an outcome, we consider data from a school district in a south-eastern U.S. state as well as that state's end-of-year ELA standardized test outcome. Generalization is explored by considering prediction performance when training and testing models on data from each of the individual school districts (and for their respective state's test outcomes) as well as pooling data from both districts together. We conclude with discussion of investigations of some algorithmic fairness characteristics of the learned models. The results suggest that a model trained on data from the smaller of the two school districts considered may achieve greater fairness in its predictions over models trained on data from the other district or both districts, despite broad, overall similarities in some demographic characteristics of the two school districts. This raises interesting questions for future research on generalizing these kinds of models as well as on ensuring algorithmic fairness of resulting models for use in real-world adaptive systems for learning.