Notice bibliographique
Résumé
A Review of: Kaneko, K., Saito, Y., Nohara, Y., Kudo, E., & Yamada, M. (2018). Does physical activity enhance learning performance? Learning effectiveness of game-based experiential learning for university library instruction. Journal of Academic Librarianship, 44(5), 569-581. https://doi.org/10.1016/j.acalib.2018.06.002 Abstract Objective – To understand the impact of a mobile application game for library knowledge acquisition, task performance, and the process of learning. Design – The main experiment included a pretest, learning experience, post-test, and a questionnaire. One month later, a post-experiment was conducted, including a test of “declarative knowledge” and a behavioural test. Setting – Kyushu University in Fukuoka, Japan Subjects – 36 first-year undergraduate students, of which 25 were female and 11 were male. Students were divided into experimental and control groups. 32 students completed the study. Methods – In the main experiment, students responded to the same 20 question pre-test on library use, and then both groups participated in learning experiences designed to convey knowledge about using the library. The control group’s learning setting was a web-based tutorial about the library. The experimental group’s learning setting was “Library Adventures: Unveil the Hidden Mysteries!” a “game-based learning environment” developed by the researchers (Kaneko, Saito, Nohara, Kudo, & Yamada, 2015, p. 404), which required students to complete activities by physically moving through the library. For both groups, learning content related to local library procedures, like hours, arrangement of collections, and methods for locating books and articles. The game collected data that the authors analyzed using statistical methods in an attempt to validate quizzes that were embedded in the game. After finishing the learning experience, all students completed the 20-question post-test, and then responded to the Instructional Materials Motivation Survey (IMMS), a questionnaire designed to gauge learning motivation using the Attention, Relevance, Confidence, and Satisfaction (ARCS) model. One month following the main experiment, all students took a test of declarative knowledge and completed a skills test. Main Results – Experimental and control group students gained about the same level of declarative knowledge. All students lost some knowledge in the one-month gap between the main and post-experiment. Students who had learned through Library Adventure were able to borrow a journal and locate a newspaper article more effectively than the control group. In contrast, tutorial users made study room reservations more quickly than the experimental group. More significantly, the IMMS instrument demonstrated that game-based learners scored higher in attention, relevance, and satisfaction than tutorial-based learners. Experimental and control group participants demonstrated the same level of confidence. Conclusion – While inconclusive about the effectiveness of games versus tutorials for acquisition and retention of knowledge, the authors concluded that game-based instructional content may foster greater learner engagement, aiding some students in understanding how to use the library in a manner superior to web-based tutorials. Librarians and instructional designers developing game-based learning experiences for novice library users may find this research informative.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,034 | 0,017 |
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 ».