Emotions Are Not Random: Machine Learning Reveals Predictable Patterns in a 21-Day Second Language Learning Trajectory
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| Title: | Emotions Are Not Random: Machine Learning Reveals Predictable Patterns in a 21-Day Second Language Learning Trajectory |
|---|---|
| Language: | English |
| Authors: | Peng Wang (ORCID |
| Source: | Educational Psychology Review. 2026 38(1). |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 35 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Adult Education |
| Descriptors: | Psychological Patterns, Second Language Learning, Adult Learning, Learning Trajectories |
| DOI: | 10.1007/s10648-026-10141-8 |
| ISSN: | 1040-726X 1573-336X |
| Abstract: | Emotion research in second language learning has largely focused on static, trait-based measures, especially anxiety, while neglecting contextual and temporal dynamics. This study reconceptualizes emotions as emergent and context-dependent, shaped by real-time interactions with learning activities and self-perceptions. Using Ecological Momentary Assessment (EMA), 92 adult learners reported their emotional states (anxiety, enjoyment, boredom), perceived proficiency, and contextual features (e.g., task modality, duration) across 6,918 learning episodes over 21 days. We identified five emotional profiles, including a dominant "Routine but Pleasant" state, challenging anxiety-centred paradigms. Emotional variability was primarily intraindividual (62-68%), with weak temporal trends and idiosyncratic cycles. Time-series machine learning (TabPFN-TS) achieved high predictive accuracy for emotional states (e.g., anxiety R² = 0.87; boredom R² = 0.95) and perceived proficiency (R² = 0.83). These findings underscore the value of modelling emotional dynamics in context and suggest that short-horizon forecasting may help identify moments when timely learner support could be explored in future work. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1510227 |
| Database: | ERIC |
| Abstract: | Emotion research in second language learning has largely focused on static, trait-based measures, especially anxiety, while neglecting contextual and temporal dynamics. This study reconceptualizes emotions as emergent and context-dependent, shaped by real-time interactions with learning activities and self-perceptions. Using Ecological Momentary Assessment (EMA), 92 adult learners reported their emotional states (anxiety, enjoyment, boredom), perceived proficiency, and contextual features (e.g., task modality, duration) across 6,918 learning episodes over 21 days. We identified five emotional profiles, including a dominant "Routine but Pleasant" state, challenging anxiety-centred paradigms. Emotional variability was primarily intraindividual (62-68%), with weak temporal trends and idiosyncratic cycles. Time-series machine learning (TabPFN-TS) achieved high predictive accuracy for emotional states (e.g., anxiety R² = 0.87; boredom R² = 0.95) and perceived proficiency (R² = 0.83). These findings underscore the value of modelling emotional dynamics in context and suggest that short-horizon forecasting may help identify moments when timely learner support could be explored in future work. |
|---|---|
| ISSN: | 1040-726X 1573-336X |
| DOI: | 10.1007/s10648-026-10141-8 |
