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Student affect has been found to correlate with short- and long-term learning outcomes, including college attendance as well as interest and involvement in Science, Technology, Engineering, and Mathematics (STEM) careers. However, there still remain significant questions about the processes by which affect shifts and develops during the learning process. Much of this research can be split into affect dynamics, the study of the temporal transitions between affective states, and affective chronometry, the study of how an affect state emerges and dissipates over time. Thus far, these affective processes have been primarily studied using field observations, sensors, or student self-report measures; however, these approaches can be coarse, and obtaining finer grained data produces challenges to data fidelity. Recent developments in sensor-free detectors of student affect, utilizing only the data from student interactions with a computer based learning platform, open an opportunity to study affect dynamics and chronometry at moment-to-moment levels of granularity. This work presents a novel approach, applying sensor-free detectors to study these two prominent problems in affective research.
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The Half-Life of Epistemic Emotions: How Motivation Influences Affective Chronometry
Research on epistemic emotions has often focused on how students transition between affective states (e.g., affect dynamics). More recently, studies have examined the properties of cases where a student remains in the same affective state over time, finding that the duration of a student's affective state is important for multiple learning outcomes. However, the likelihood of remaining in a given affective state has not been widely studied across different methods or systems. Additionally, the role of motivational factors in the persistence or decay of affective states remains underexplored. This study builds on two prior investigations into the exponential decay of epistemic emotions, expanding the analysis of affective chronometry by incorporating two detection methods based on student self-reports and trained observer labels in a game-based learning environment. We also examine the relationship between motivational measures and affective decay. Our findings indicate that boredom exhibits the slowest decay across both detection methods, while confusion is the least persistent. Furthermore, we found that higher situational interest and self-efficacy are associated with greater persistence in engaged concentration, as identified by both detection methods. This work provides novel insights into how motivational factors shape affective chronometry, contributing to a deeper understanding of the temporal dynamics of epistemic emotions.
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- Award ID(s):
- 2016993
- PAR ID:
- 10634718
- Editor(s):
- Mills, Caitlin; Alexandron, Giora; Taibi, Davide; Lo_Bosco, Giosuè; Paquette, Luc
- Publisher / Repository:
- International Educational Data Mining Society
- Date Published:
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Attribution 4.0 International
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.5281/zenodo.15870172
