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| Autor principal: | |
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| Format: | Recurso digital |
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| Publicat: |
Zenodo
2026
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| Accés en línia: | https://doi.org/10.5281/zenodo.18814532 |
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Taula de continguts:
- <p>This dataset contains anonymized results from a pilot feasibility study (N=10) evaluating the Adaptive Contextual Task Engine (ACTE) algorithm—a mastery-aware, location-based task recommender for mobile language learning. The study was conducted in a simulated café environment with university students (aged 18–23) practicing A2-level English speaking tasks. Data includes demographics, System Usability Scale (SUS) responses, task relevance ratings, and open-ended feedback.</p> <p>The ACTE algorithm is designed to support "Contextual Immersion Learning," where language practice is triggered by real-world semantic contexts (e.g., cafés, hospitals) and structured around CEFR-aligned modules, badge-based mastery, and time-sensitive performance scoring.</p>