Increased Contrast Improves Neuroanatomy Learning for Those with Low Working Memory
Notice bibliographique
Résumé
Introduction Anatomy education is burdened by neurophobia, a student’s fear when learning neuroanatomy and clinical neurology. While neurophobia’s cause remains unknown, poor definition of structures in standard neurological specimens may affect students’ learning due to influences on cognitive load. In this study, we examined the effects of increased visual contrast between grey and white matter ‐ done via novel staining method ‐ with the goal of providing insight into how educators can adapt anatomical specimens to aid students during learning and application. Aim To determine if increasing contrast between grey and white matter on human brain slices aids students in learning neuroanatomy, and if the difference in contrast improves students’ performance during testing. Methods Undergraduate students at McMaster University with no prior neuroanatomical education (n = 102) were recruited for a 3‐day protocol. On Day 0, participants learned 12 neuroanatomical structures from a set of brain slices (transverse or coronal section, with low or high contrast). They were then asked to locate and recall these structures on unstained slices of the same section. The learn‐test phase was immediately repeated with a second set of slices counterbalancing section and contrast. Participants returned for a learning‐only session 24 hours later (Day 1) using the same specimens from Day 0, and were tested on unstained sets 48 hours after (Day 2). Participants then completed an Automated Operation Span Task (OSPAN) to assess working memory capacity (WMC), and a learning methods survey. Results Repeated Measures ANOVA tests were performed using Time (Day 0 v. Day 2) and Staining (stained v. unstained) as within‐subject variables. Though Time had an effect, with Day 0 performance being significantly better than that of Day 2 ( F (1, 101) = 26.93, p < .001, ηp 2 = 0.21), there was no effect of Staining. Participants performed relatively equally ( F (1, 101) = 0.234, p = .629, ηp 2 = .002). Participant data were sorted into quartiles based on WMC (OSPAN score). Independent sample t‐tests of the sorted data showed that participants with low WMC performed significantly worse than those with high WMC when learning from low‐contrast specimens, regardless of time ( t (47) = −2.164, p = .036, d = (5.58−4.04) ⁄ 2.49 = .618). However, performance between these groups was equalized after learning from high‐contrast slices ( t (47) = −0.53, p = .596, d = (5.38−4.92) ⁄ 2.98 = .154). Discussion/Conclusion Results show that increased contrast had little effect within individual results; however, it improved overall performance of low WMC participants, helping to match results of high WMC participants. This suggests that the use of high‐contrast brain slices may prove beneficial when teaching students with low WMC that struggle with neuroanatomy, potentially by reducing the cognitive load needed to locate structures so students can re‐allocate cognitive capacity towards learning. Further insight may be gained using eye‐tracking technology to observe student gaze patterns as they view the material to determine how structures are being viewed.
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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 | 0,001 |
| 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 ».