A Study in Blue: Does Increasing Contrast of Brain Slices Improve Neuroanatomy Learning?
Notice bibliographique
Résumé
Introduction Neurophobia is the fear many students experience when faced with the task of learning neuroanatomy. This problem may partly result from the large number of poorly defined structures observed in unstained brain slices. There is little discussion in educational literature regarding the most effective way to learn neuroanatomy. In particular, the effect of increasing the contrast between gray and white matter on student learning has not been examined. We hypothesized that some neurophobia, particularly among those with low working memory, might be mitigated with our recently developed brain slice staining method. Aim To determine if increasing the contrast between gray and white matter on human brain slices would improve students' ability to learn neuroanatomy, and if the intervention especially helped students with a lower working memory capacity. Methods Participants were recruited from an introductory psych course at McMaster University and were required to not have any previous neuroanatomical education. Participants learned and were tested on 12 neuroanatomical structures each from two sets of brain slices (transverse stained and coronal unstained, or coronal stained and transverse unstained) on day 1, participated in a restudy period 24 hours later on day 2, and were tested again 48 hours later on day 3. In addition, all participants completed the Automated Operation Span Task (OSPAN) working memory test and a survey on learning methods. Results While data collection is still ongoing, interim analysis has been completed. A 2×2 repeated measures ANOVA revealed that there was no difference in test performance between stained and unstained slices on day 1 or day 3 in general (p=0.785). A linear regression analysis for both the stained and unstained groups revealed that working memory capacity was a significant predictor of performance in the unstained condition (p=0.001). However, working memory was not a predictor of performance in the stained condition (p=0.123). Students with low working memory performed 11.4% worse on unstained brain slices than on stained slices, while students with high working memory showed no significant difference. Discussion and Conclusion While data collection continues, the preliminary results reveal interesting possibilities. Initially, staining did not appear to facilitate neuroanatomy learning. However, students with low working memory showed a significant improvement when learning from stained brain slices compared to unstained. Therefore, the use of stained brain slices in the teaching of neuroanatomy may partially mitigate neurophobia, improve teaching, and particularly aid those students already struggling with the subject. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0,000 | 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 | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».