21-day EMA dataset of 6,918 language-learning episodes with momentary emotions, perceived proficiency and contextual features

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Evidence · 2 claims (2 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 2 claims rest on one study

Description

A smartphone-based intensive longitudinal dataset from 92 adult foreign-language learners, capturing momentary emotions, perceived proficiency, and contextual features across 21 days.

Components

Claims

• Adult L2 learners' momentary emotions cluster into five profiles, dominated by a low-arousal 'Routine but Pleasant' state (57.8% of episodes), challenging anxiety-centred paradigms [+M]
• Time-series forecasting with TabPFN-TS markedly improves prediction of next-episode emotions and perceived proficiency over a static model (e.g., anxiety R² 0.458→0.866; boredom 0.596→0.953) [+M]

Related Products and Programmes

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Key Sources

• Peng Wang, Lesya Ganushchak, Camille Welie, Roel van Steensel. (2026). Emotions Are Not Random: Machine Learning Reveals Predictable Patterns in a 21-Day Second Language Learning Trajectory. Educational Psychology Review. https://doi.org/10.1007/s10648-026-10141-8

Page metadata

Field	Value
type	product
id	21-day-ema-dataset-of-6918-language-learning-episodes-with-momentary-emotions-perceived-pr
title	21-day EMA dataset of 6,918 language-learning episodes with momentary emotions, perceived proficiency and contextual features
description	A smartphone-based intensive longitudinal dataset from 92 adult foreign-language learners, capturing momentary emotions, perceived proficiency, and contextual features across 21 days.
status	review
product_kind	dataset
generated	by process:settle-candidates on 2026-10-21
trust	unverified — no one has confirmed this page's content

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