“Applying anatomy to something I care about”: Authentic inquiry learning and student experiences of an inquiry project
Bibliographic record
Abstract
Despite advances to move anatomy education away from its didactic history, there is a continued need for students to contextualize their studies to make learning more meaningful. This article investigates authentic learning in the context of an inquiry-based approach to learning human gross anatomy. Utilizing a case-study design with three groups of students (n = 18) and their facilitators (n = 3), methods of classroom observations, interviews, and artifact collection were utilized to investigate students' experiences of learning through an inquiry project. Qualitative data analysis through open and selective coding produced common meaningful themes of group and student experiences. Overall results demonstrate how the project served as a unique learning experience where learners engaged in the opportunity to make sense of anatomy in context of their interests and wider interdisciplinary considerations through collaborative, group-based investigation. Results were further considered in context of theoretical frameworks of inquiry-based and authentic learning. Results from this study demonstrate how students can engage anatomical understandings to inquire and apply disciplinary considerations to their personal lives and the world around them. Anat Sci Educ 10: 538-548. © 2017 American Association of Anatomists.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".