Motivation in computer‐assisted instruction
Notice bibliographique
Résumé
OBJECTIVES/HYPOTHESIS: Computer-aided instruction (CAI) is defined as instruction in which computers play a central role as the means of information delivery and direct interaction with learners. Computer-aided instruction has become mainstream in medical school curricula. For example, a three-dimensional (3D) computer module of the larynx has been created to teach laryngeal anatomy. Although the novelty and educational potential of CAI has garnered much attention, these new technologies have been plagued with low utilization rates. Several experts attribute this problem to lack of motivation in students. Motivation is defined as the desire and action toward goal-oriented behavior. Psychologist Dr. John Keller developed the ARCS theory of motivational learning, which proposed four components: attention (A), relevance (R), concentration (C), and satisfaction (S). Keller believed that motivation is not only an innate characteristic of the pupil; it can also be influenced by external factors, such as the instructional design of the curriculum. Thus, understanding motivation is an important step to designing CAI appropriately. Keller also developed a 36-item validated instrument called the Instructional Materials Motivation Survey (IMMS) to measure motivation. The objective of this study was to study motivation in CAI. Medical students learning anatomy with the 3D computer module will have higher laryngeal anatomy test scores and higher IMMS motivation scores. Higher anatomy test scores will be positively associated with higher IMMS scores. STUDY DESIGN: Prospective, randomized, controlled trial. METHODS: After obtaining institutional review board approval, 100 medical students (mean age 25.5 ± 2.5, 49% male) were randomized to either the 3D computer module (n = 49) or written text (n = 51). Information content was identical in both arms. Students were given 30 minutes to study laryngeal anatomy and then completed the laryngeal anatomy test and IMMS. Students were categorized as either junior (year 1 and 2) or senior (year 3 and 4). RESULTS: There were no significant differences in anatomy scores based on educational modality. There was significant interaction of educational modality by year [F(1,96) = 4.12, P = 0.045, ω(2) = 0.031]. For the total score, there was a significant effect of year [F(1,96) = 22.28, P < 0.001, ω(2) = 0.178], with seniors (15.4 ± 2.6) scoring significantly higher than juniors (12.8 ± 3.1). For the motivational score, the total IMMS score had two significant effects. With educational modality [F(1,96) = 5.18, P = 0.025, ω(2) = 0.041], the 3D group (12.4 ± 2.8) scored significantly higher than the written text group (11.7 ± 3.2). With year [F(1,96) = 25.31, P < 0.001, ω(2) = 0.198], seniors (13.4 ± 3.0) scored significantly higher than juniors (10.8 ± 2.5). Pearson's correlation showed positive associations (r = 0.22-0.91) between anatomy scores and IMMS motivation scores (P < 0.05). CONCLUSION: Computer-aided instruction conferred no measurable educational benefit over traditional written text in medical students; however, CAI was associated with higher motivational levels. Computer-aided instruction was found to have a greater positive impact on senior medical students with higher anatomy and motivational scores. Higher anatomy scores were positively associated with higher motivational scores. Computer-aided instruction may be better targeted toward senior students. LEVEL OF EVIDENCE: N/A. Laryngoscope, 126:S5-S13, 2016.
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 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) | 0,000 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».