“This Is How You Do It, Right?!” Practical Suggestions for Designing and Implementing Education Research Projects within the Anatomy Classroom
Bibliographic record
Abstract
Anatomy educators have become leaders in the Scholarship of Teaching and Learning (SoTL) within the biomedical sciences. Our classrooms and laboratories are learning hubs filled with innovative, evidence‐based pedagogies that we commonly systematically evaluate for learning effectiveness. As we continue to push the boundaries of innovative teaching, we must be careful to ensure we are developing, implementing, and disseminating education research projects with strong and resilient designs. Within the broad field of SoTL and education research there are common design flaws that can be avoided with careful preparation and forethought. Specifically, this presentation will focus on six themes of education research each with common concerns: 1) selecting an appropriate methodology and data collection strategy that best aligns with the specific research question(s), 2) establishing appropriate and informative comparison groups, 3) using randomization to control for between‐student factors (and to avoid self‐selection bias), 4) including appropriate considerations of power differential when using one's own students as research participants, 5) obtaining informed consent in a manner that respects student autonomy, and 6) selecting robust statistical analyses that fit the research design. Each theme will be thoroughly expanded upon in the context of the anatomy classroom/laboratory with concrete, practical suggestions for effective and meaningful SoTL projects. Positive examples will be extracted from recently published works to showcase exemplary research designs in the field. Ultimately, the goal of this presentation is to initiate and fuel constructive conversations on education research design elements; a conversation that will benefit junior and senior anatomy educators alike. By encouraging and facilitating strong, resilient, and thoughtful education research projects, anatomy educators can continue to be at the forefront of innovative SoTL practices that enhance learning for our students and positively impact biomedical education at large. Support or Funding Information n/a This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".