Gamified Mobile Collaborative Location-Based Language Learning
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
As design-based research, this study describes the development and analysis of two location-based augmented reality (AR) serious learning games (SLG) for French second language (FL2) learning. Explorez and VdeUVic are collaborative quest-based SLGs. At different locations on campus, players interact with characters that give them quests including clues or options to further the storyline. These interactions take place in the form of either written text, or audio and video recordings, encouraging students to develop language skills both written and oral. Students choose their own learning path and advance at their own pace. Three cohorts of FL2 university students play-tested the games, with 58 of the 77 students choosing to participate in the study. The design-based research framework for the development of the game iterations and subsequent testing was an iterative process with each stage producing output that became input for the next stage. The evaluation of the AR language tools was implemented by means of a mixed-method case study, collecting data of both a qualitative and quantitative nature, through pre and post-play questionnaires, interviews, and video recordings of student gameplay interactions for analysis. Informed by situated cognition, one of the goals was to provide a contextual and immersive learning experience. Additionally, this research drew on sociocultural theory and the social nature of language learning, emphasizing learner interactions as a principal learning force. This research examined the learners’ perceptions of their learning experience, as well as the ways in which students collaborated to complete the tasks. Employing a situative approach framework informed by social regulation and content processing, student learning patterns were examined. Distinct types of learner interactions amongst teams during gameplay were shown. Patterns in the emergence of learners’ high-level co-regulation during collaborative learning are indicated in the findings. Key elements for the development and implementation of location-based serious games to foster collaborative learning are highlighted.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 découle