Learning through the Eyes of the Beholder: Using Eye Tracking to Understand How Novices Learn Neuroanatomy
Notice bibliographique
Résumé
Introduction Despite student reports of high difficulty and neurophobia, there remains little examination in the literature on what makes neuroanatomy challenging. A preliminary study found that increased grey‐to‐white matter stain contrast marginally improves learning in students with low working memory capacities (WMC) but not those with high WMC. Why this augmentation is beneficial to some but not all students is unknown. Further, evidence suggests competency influences the way images are viewed and interpreted, which may account for some this previously observed difference. Aim This study will use eye tracking to better understand how novice students view and learn neuroanatomy. Specifically, we aim to: 1) assess differences in viewing patterns between high and low contrast brain slices for students with high and low WMC, and 2) to then compare viewing patterns between novices and experts to uncover competency‐related changes as they relate to contrast. Methods Undergraduate students with no previous anatomical education (n=120) were recruited to complete an eye‐tracking session containing two learning and two testing periods. Each learning period consisted of 4 brain slices (either coronal or transverse planes and high or low contrast) labeled with 12 neuroanatomical structures. The students were given 5 minutes to learn the structures while their viewing patterns were tracked. In the testing periods, students were prompted to click on named structures on a low‐contrast brain slice. Eye tracking data was recorded along with test accuracy. This learning/testing protocol was repeated twice such that the brain image sectioning plane (coronal vs transverse) and the stain contrast (high vs low) order of exposure were counterbalanced. Finally, participants completed the Automated Operation Span Task (OSPAN) to quantify their WMC. Results Data collection is ongoing. Preliminary results suggest differences in fixation durations, frequency of fixations on irrelevant areas, and time to first fixate on relevant structures between novices and experts. Full data will be subsequently analyzed and presented. Discussion/Conclusion By better understanding how students learn neuroanatomy, we will be able to gain insight into factors that contribute to the difficulty of the subject and support research on better methods for teaching neuroanatomy. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0,001 | 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 ».