Design of a novel approach to teaching online neuroanatomical education (725.1)
Bibliographic record
Abstract
E‐learning allows for the development of educational tools that present information in different modalities suited to each student's unique needs. However, the implementation of such tools is occurring in advance of adequate empirical research on how students utilize and learn from such instructional resources. Research suggests that the effectiveness of any instructional tool depends on how well its design reflects human cognitive architecture. We hypothesize the implementation of a neuroanatomy e‐learning curriculum that takes students’ different learning abilities and preferences into consideration by incorporating equivalent material presented in multiple modalities (2D, 3D, and text), and is congruent with the principles of Cognitive Load Theory, will be more effective, and will result in improved student learning. Learning outcomes will be assessed using a pre‐test/post‐test knowledge measure. The student’s visuo‐spatial abilities will also be measured, and will be compared with each student’s self‐directed “e‐learning path”. A qualitative measure will also be administered to assess student satisfaction and preference for different modalities. Results of this study may be used to help establish guiding principles that will facilitate the design and implementation of effective and efficient e‐learning curricula tools that complement students’ unique learning strengths and needs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".