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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 0000-0002-3475-1561), Lesya Ganushchak (ORCID 0000-0003-2170-5061), Camille Welie (ORCID 0000-0002-8534-240X), Roel van Steensel
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: 20260101
Accession Number: EJ1510227
Database: ERIC
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  Data: Emotions Are Not Random: Machine Learning Reveals Predictable Patterns in a 21-Day Second Language Learning Trajectory
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  Data: <searchLink fieldCode="AR" term="%22Peng+Wang%22">Peng Wang</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-3475-1561">0000-0002-3475-1561</externalLink>)<br /><searchLink fieldCode="AR" term="%22Lesya+Ganushchak%22">Lesya Ganushchak</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-2170-5061">0000-0003-2170-5061</externalLink>)<br /><searchLink fieldCode="AR" term="%22Camille+Welie%22">Camille Welie</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-8534-240X">0000-0002-8534-240X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Roel+van+Steensel%22">Roel van Steensel</searchLink>
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  Data: 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/
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  Data: 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.
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