The Use of Visuals in Undergraduate Neuroscience Education: Recommendations for Educators
Bibliographic record
Abstract
Introduction: There is a history of overlap between art and science education, particularly in anatomy and other related medical specialties. Technological advances have increased exposure to visual images and creation and sharing of image-based content is commonplace. Statement of the Problem: The use of visual content and activities in education typically declines after early childhood, after which most teaching and learning relies heavily on text-based curricula. Incorporating visual content into education makes optimal use of human cognition; visual and verbal processing channels can operate independently, so using both allows for dual coding and enhanced memory. Literature Review: In this paper, we review the literature on the use of visual techniques in teaching undergraduate neuroscience. Teaching Implications: Image-based content can offer learners an additional cognitive resource and also engage English language learners and those with reading challenges, which might not benefit as much from a solely text-based approach. Conclusion: We recommend educators consider the use of (1) learner-generated drawing, (2) 3-D modeling, and (3) infographics to improve learning outcomes among undergraduate neuroscience students. We provide resources and practical suggestions for implementing the aforementioned techniques.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".