Cerebro, the Virtual Lab ‐ a mobile neuroanatomy game that is both fun and educational
Bibliographic record
Abstract
Neuroanatomy remains one of the more complex disciplines in anatomical sciences education. It requires both a deep understanding of 3D anatomical relationships within the brain and a translation of that knowledge to 2D slices through the brain – an important skill, which enables the interpretation of clinical imaging studies of the brain. An additional challenge for learners is to understand the anatomy of the main ascending and descending tracts through the brainstem as well as the location of the crossing to the contralateral side for these pathways. Mastery of neuroanatomy requires both a conceptual understanding and practice in identifying structures with confidence on slices through the brainstem and the forebrain. Gamification of learning is a concept that aims to harness the fun and rewarding qualities of a game to make learning appear effortless. In this project we put together a multidisciplinary team of neuroanatomy content experts, game & software designers, and artists to develop a neuroanatomy game that is easy to use and yields learning results for students. The target audience was identified and characterized and the user experience and user interface were designed for this target audience. Iterative, agile design cycles with multiple sessions of user testing resulted in a final product that serves the needs of undergraduate students seeking a high yield return on their time investment for learning. A narrative arc was developed for the game and chapters with three levels from beginner to expert were created based on the learning objectives of typical neuroanatomy lab sessions. Principles of design thinking and iterative software design were applied in the creation of this game so that the final product would meet both the learning objectives of the course and the objective to keep the game fun and competitive. The game was designed to fit into the existing learning ecosystem for neuroanatomy, which includes interactive online resources, 3D experiences, and lab sessions that are integrated with clinical sciences.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".