Learning through the Eyes of the Beholder: Using Eye Tracking to Understand How Novices Learn Neuroanatomy
Bibliographic record
Abstract
Introduction Despite student reports of high difficulty and neurophobia, there remains little examination in the literature on what makes neuroanatomy challenging. A preliminary study found that increased grey‐to‐white matter stain contrast marginally improves learning in students with low working memory capacities (WMC) but not those with high WMC. Why this augmentation is beneficial to some but not all students is unknown. Further, evidence suggests competency influences the way images are viewed and interpreted, which may account for some this previously observed difference. Aim This study will use eye tracking to better understand how novice students view and learn neuroanatomy. Specifically, we aim to: 1) assess differences in viewing patterns between high and low contrast brain slices for students with high and low WMC, and 2) to then compare viewing patterns between novices and experts to uncover competency‐related changes as they relate to contrast. Methods Undergraduate students with no previous anatomical education (n=120) were recruited to complete an eye‐tracking session containing two learning and two testing periods. Each learning period consisted of 4 brain slices (either coronal or transverse planes and high or low contrast) labeled with 12 neuroanatomical structures. The students were given 5 minutes to learn the structures while their viewing patterns were tracked. In the testing periods, students were prompted to click on named structures on a low‐contrast brain slice. Eye tracking data was recorded along with test accuracy. This learning/testing protocol was repeated twice such that the brain image sectioning plane (coronal vs transverse) and the stain contrast (high vs low) order of exposure were counterbalanced. Finally, participants completed the Automated Operation Span Task (OSPAN) to quantify their WMC. Results Data collection is ongoing. Preliminary results suggest differences in fixation durations, frequency of fixations on irrelevant areas, and time to first fixate on relevant structures between novices and experts. Full data will be subsequently analyzed and presented. Discussion/Conclusion By better understanding how students learn neuroanatomy, we will be able to gain insight into factors that contribute to the difficulty of the subject and support research on better methods for teaching neuroanatomy. 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 | 0.000 | 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 | 0.001 | 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".