Students as partners: A novel approach to developing a gamified anatomical learning toolkit using design thinking principles
Bibliographic record
Abstract
The practice of involving students in designing pedagogical resources, including gamified learning tools, is often underutilized. Traditionally, students are engaged in usability and efficacy testing of finalized learning tools, limiting their ability to shape the learning experience from inception. However, adopting a Students as Partners (SAP) approach allows for their involvement earlier and throughout the design and development process. Design thinking offers a structured methodology to optimize this partnership, providing a learner-centered approach to creating gamified learning tools with students for students. This process fosters a deep understanding of students' needs (empathizing and defining learners' needs), incorporates their ideas (challenging assumptions and idea creation), and enables iterative feedback (involvement in prototyping and testing). In this article, we describe how the design thinking methodology, in collaboration with SAP, was utilized to develop Anat-O-MEE, a gamified learning toolkit designed to enhance students' three-dimensional (3D) spatial reasoning skills in anatomy. The toolkit consists of three scaffolded levels-Map, Explore, and Extrapolate-which progressively support the transition from 2D to 3D anatomical understanding. Student partners played an active role in user-interface testing, functionality assessments, and alpha testing of games and tasks, contributing to iterative refinements in both design and content.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".