Adaptive Contextual Task Engine (ACTE): A Mastery-Aware Task Recommender for Mobile Language Learning in Real-World Contexts
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| Format: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901972017217536 |
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| author | Purwanto, Yudhy S. |
| author_facet | Purwanto, Yudhy S. |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18814532 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Adaptive Contextual Task Engine (ACTE): A Mastery-Aware Task Recommender for Mobile Language Learning in Real-World Contexts Purwanto, Yudhy S. <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> |
| title | Adaptive Contextual Task Engine (ACTE): A Mastery-Aware Task Recommender for Mobile Language Learning in Real-World Contexts |
| url | https://doi.org/10.5281/zenodo.18814532 |