Impact of integrating interactive neuroanatomy e‐learning resources on novice student learning
Bibliographic record
Abstract
Neuroanatomy is one of the most complex topics in anatomy with many novice students reporting they consider it to be the most difficult component in their anatomy curriculum. A main reason for the reported difficulties is the high spatial complexity that exists between structures. The development of interactive three‐dimensional (3D) models in neuroanatomy curricula may provide an effective solution for alleviating difficulties students encounter while learning with conventional two‐dimensional (2D) resources, however this has yet to be fully examined in the literature. Interactive 3D and 2D e‐learning resources were developed to complement undergraduate neuroanatomy instruction. The 3D module provided students the opportunity to manipulate a dynamic 3D model in order to view structures from any desired angle, view deep cortical structures at high magnification, and add interactive structural labels. One hundred forty‐four participants completed the study, which utilized a cross‐over design to separate participants into two groups. Each group initially completed an anatomy knowledge pretest, followed by access to either the 3D or 2D neuroanatomy e‐learning resource. Participants completed a first post‐module assessment prior to switching to the other e‐learning resource. A second post‐module assessment was administered following participants' exposure to the second learning resource. Data was also collected for the time spent by participants using each learning modality. Participants who initially accessed the 3D e‐learning resource had a significantly greater increase in score between the pretest and the first post‐module assessments than the students who initially accessed the 2D e‐learning resource. Participants who viewed the 3D e‐learning resource following access to the 2D e‐learning resource significantly improved their scores between the first and second post‐module assessments. Total time spent using both e‐learning resources did not significantly differ between groups. Participants who initially accessed the 3D resource spent equivalent amounts of time using each resource, whereas participants who initially accessed the 2D resource spent significantly more time using the 3D resource than the 2D resource. Results of this study could be used to inform the effective development and implementation of 3D e‐learning resources to improve neuroanatomy instruction and student learning outcomes. Support or Funding Information Social Sciences and Humanities Research Council
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".