Flipping the neuroanatomy labs: how the production of high quality video and interactive modules changed our approach to teaching (211.3)
Bibliographic record
Abstract
Neuroanatomy is often approached with apprehension, often described as “neurophobia”. The result has been a triage approach by students: memorizing as much information as possible to pass the exam, and relegating a deep understanding of CNS systems as they relate to the clinical reality to clinical experiences. Aware of this reality, we wanted to create content that is accessible and engaging; moreover, we wanted to “flip” the classroom so that students could begin to use class time for knowledge application instead of memorization. The theory behind a flipped classroom approach is to provide resources to the students to prepare with prior to coming to class, and then use the classroom time for the application of this knowledge to clinical cases and in‐depth discussions about CNS systems. We created eight highly produced mini‐documentaries to provide conceptual overviews of key brain systems, and 20 interactive modules for more in‐depth didactic content as well as formative assessment for the students. All of these resources are posted online under a Creative Commons license. A Readiness Assessment Test (RAT) at the beginning of the session gauges student understanding of the material; lab time is then used to address areas of weakness as well as to apply knowledge to clinical cases ‐ a core focus of each lab. Evidence suggests that this approach can make the classroom experience more engaging for both faculty and students. Grant Funding Source : Supported by: UBC Flexible Learning Initiative
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".