Adapting Language Learning Materials for Digital Native: Infusing CEFR Standards in English Procedural Texts on Microlearning Apps
Notice bibliographique
Résumé
This study focuses on developing an educational application that marries English procedural text materials to microlearning techniques, designed specifically for digital native learners in accordance with the CEFR. It will lay down how to adapt conventional procedural text learning into a form based on microlearning. Using a descriptive case study research design, the study critically explored the processes involved in the design and development of the application, emphasizing strategic reconfigurations to integrate microlearning principles. These reconfigurations include segmenting complex instructional content into bite-sized, manageable learning units made richly interactive and multimedia-infused, which are necessary for addressing the abbreviated attention spans and digital preferences of modern learners. These results indicated that such adaptations were not only learners' engagement and understanding but also very successful in reinforcing language competence according to the CEFR standards. This study was designed to give a holistic frame for educators and developers in the area of effective microlearning materials that fit both educational content, and learning habits and needs of modern, digital-oriented students.Objective: The aims of this study are to explore the processes involved in the design and development of the application, emphasizing strategic reconfigurations to integrate microlearning principles.Methods: This study employed a descriptive case study design to integrate microlearning principles into CEFR-aligned language materials. Using a needs analysis with 22 teachers and 19 students, data collection involved structured interviews and document analysis of prototypes. Thematic analysis identified patterns to guide the redesign process, ensuring materials were user-centered and effective. Ethical protocols, including informed consent and data anonymization, safeguarded participants. This approach highlighted microlearning's impact on enhancing engagement and aligning educational content with CEFR standards.Results: The study demonstrates MaMo's effectiveness in integrating CEFR standards with microlearning principles for procedural text learning. Key features, including simplified micro-competencies, short-duration modules, interactive content, and flexible learning paths, enhanced comprehension and retention. Its genre-based approach, structured into five activities, supported learners in achieving CEFR outcomes, particularly in oral and reading comprehension. The app’s user-friendly design, multimedia integration, and clear objectives fostered learner engagement and autonomy, aligning with the needs of modern learners. These results highlight MaMo as an innovative tool for advancing structured and effective language proficiency.Conclusions: MAMO integrates microlearning principles with CEFR standards to provide an engaging, mastery-based platform for A1 to B2 learners. Its focus on manageable activities, autonomy, and real-world application makes it effective for language skill development, especially for Generation Z learners. Future research could explore advanced proficiency levels, AI-driven personalization, multilingual support, and collaborative tools. Longitudinal and comparative studies could further refine MAMO’s impact, solidifying its role as an innovative tool in language education.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».