Quantifying Two Dimensional (2D) and Three Dimensional (3D) Anatomical Learning Using a Neuroeducational Approach
Notice bibliographique
Résumé
Background Advances in computer visualization enabling both 2D and 3D representation have generated tools to aid perception of spatial relationships and provide a new forum for instructional design. To date, studies examining the effectiveness of these educational tools have been comparative, using performance measurement as proxy variables for learning. A key knowledge gap in the field of health professional education is the lack of understanding of how the brain processes and learns from spatially presented content. Objective To use a reinforcement‐based learning activity to assess learning with 2D and 3D (stereoscopic) anatomical representations by comparing event‐related brain potentials (ERPs) as measured by electroencephalography (EEG). Methods Mean ERP waveforms of two ERP components, N250 (related to object perception) and reward positivity (related to learners responding to positive feedback), were compared as novice participants (n = 61) learned to identify and localize neuroanatomical structures. Participants learned from 2D, 3D or a combination of 2D and 3D models. Results N250 is significantly greater when participants view 3D versus 2D represented anatomical images. Behavioural learning curves and reward positivity did not differ based on model type used during initial learning. However, interleaved learning incorporating 2D and 3D models provided an advantage in retention and transfer activities represented by decreased reward positivity. Conclusion Despite the lack of difference in behavioural‐based learning efficiency outcomes for 2D versus 3D models, neural measures reveal new insights. Greater object recognition was noted for participants learning from 3D models and interleaved training using both 2D and 3D model types provides advantages for memory retention. These new insights should be kept in mind as educators are designing learning activities in the anatomical sciences. Validation of quantitative neurophysiological variables that measure learning will enable a direct measure of knowledge acquisition that can be used to strategically assess and optimize new forms of teaching, learning, and evaluation. Support or Funding Information This research was supported by University of Calgary grants (competitive) awarded to the authors including: Teaching and Learning Grant; University Research Grants Committee (URGC) Seed Grant; and the Data and Technology Fund. SA would like to acknowledge scholarship funding provided by: Social Sciences and Humanities Research Council (SSHRC) Doctoral Fellowship; Alberta Innovates Health Solutions (AIHS) Graduate Studentship, and the Queen Elizabeth II Graduate Doctoral Scholarship. This abstract is from the Experimental Biology 2018 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 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 | 0,001 | 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 ».