Numerous studies aim to enhance learning in digital environments through emotionally-sensitive interventions. The D’Mello and Graesser (2012) model of affect dynamics hypothesizes that when a learner encounters confusion, the degree to which it is prolonged (and transitions into frustration) or resolved, significantly affects their learning outcomes in digital environments. However, studies yield inconclusive results regarding relations between confusion, frustration, and learning. More research is needed to explore how confusion and frustration manifest during learning and its relation to outcomes. We go beyond past work looking at the rate, duration, and transitions of confusion and frustration by treating these affective states as non-linear dynamical systems consisting of expressive and behavioral components. We examined the frequency and recurrence of facial expressions associated with basic emotions (as automatically labeled by AffDex, a standard tool for analyzing emotions with video data) during confused and frustrated states (as automatically labeled with BROMP-based detectors applied to students’ interaction data). We compare these co-occurring patterns to learning outcomes (pre-tests, post-tests, and learning gains) within a digital learning environment, Betty’s Brain. Results showed that the frequency and recurrence rate of basic emotions expressed during confusion and frustration are complex and remain incompletely understood. Specifically, we show that confusion and frustration have different relationships with learning outcomes, depending on which basic emotion expressions they co-occur with. Implications of this study open avenues for better understanding these emotions as complex and non-linear dynamical systems, in the long-term enabling personalized feedback and emotional support within digital learning environments that enhance learning outcomes.

The study investigates the impact of cognitive biases on middle-school students' affective experiences while learning about math in a game-based learning environment (GBLE). The study focused on students' confrustion, an affect construct that unifies the several manifestations of confusion and frustration. We studied confrustion in the context of students self-explaining erroneous examples, where they had to find and fix common errors in given math problems and self-explain their problem-solving processes either with or without scaffolding. Text replays were utilized to examine student interactions during game-based learning and identify behaviors that emerged in response to cognitive biases and affect and its impact on learning and performance outcomes. The results revealed that students who demonstrated more pseudo-confidence in their self-explanations had higher self-reported self-efficacy, but were more likely to submit incongruent responses, exhibit confrustion, make errors, and take longer to finish the game. Overall, the findings show that students were vulnerable to cognitive biases and did not always respond in ways that accurately reflected their approach to solving math problems. The insights into how students approach and learn from math games inform the design and implementation of GBLEs by addressing cognitive biases.

Affect dynamics, the study of how affect develops and manifests over time, has become a popular area of research in affective computing for learning. In this article, we first provide a detailed analysis of prior affect dynamics studies, elaborating both their findings and the contextual and methodological differences between these studies. We then address methodological concerns that have not been previously addressed in the literature, discussing how various edge cases should be treated. Next, we present mathematical evidence that several past studies applied the transition metric (L) incorrectly - leading to invalid conclusions of statistical significance - and provide a corrected method. Using this corrected analysis method, we reanalyze ten past affect datasets collected in diverse contexts and synthesize the results, determining that the findings do not match the most popular theoretical model of affect dynamics. Instead, our results highlight the need to focus on cultural factors in future affect dynamics research.

Education research has explored the role of students' affective states in learning, but some evidence suggests that existing models may not fully capture the meaning or frequency of how students transition between different states. In this study we examine the patterns of educationally-relevant affective states within the context of Betty's Brain, an open-ended, computer-based learning system used to teach complex scientific processes. We examine three types of affective transitions based on similarity with the theorized D'Mello and Graesser model, transition between two affective states, and the sustained instances of certain states. We correlate of the frequency of these patterns with learning outcomes and our findings suggest that boredom is a powerful indicator of students' knowledge, but not necessarily indicative of learning. We discuss our findings within the context of both research and theory on affect dynamics and the implications for pedagogical and system design.

This paper continues prior work conducted on the analysis of moments of student learning in Physics Playground, a learning environment for qualitative physics. The study analyzed data from 60 tenth-grade students who used Physics Playground for 90 minutes. We detected spikes of student learning, or “eureka” moments, while solving physics problem. We then related these moments of insight to affective states labeled according to the BROMP protocol. Our hypothesis was that students would be confused before eureka moments, and experience delight afterwards. Contrary to this hypothesis, we found that eureka moments and the periods surrounding them were associated with a decrease in all affective states. No significant differences were found in comparing the affective profiles of students who experienced eureka and those who did not.