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Employing Eye Tracking Technology to Understand How Novices Learn Neuroanatomy

2020· article· en· W3016716226 on OpenAlexaff
Angela Nguyen, Beata Cheung, Rebecca Leclair, Jessica Saini, Jim Lyons, Bruce Wainman, Jennifer J. Heisz, Danielle Brewer‐Deluce

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsQueen's UniversityMcMaster University
Fundersnot available
KeywordsNeuroanatomyContrast (vision)Eye trackingPsychologySet (abstract data type)Task (project management)Session (web analytics)GazeTracking (education)Cognitive psychologyNeuroscienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Introduction Neurophobia is the fear of neuroscience experienced by students who are learning and applying content. Although the cause remains unclear, neurophobia presents a major barrier to student success in neuroanatomy. A preliminary study suggested that individuals with low working memory capacity (WMC) benefit from using high‐contrast anatomical images when learning neuroanatomy. However, the reason for the improvement is unknown. Aim This study uses eye tracking technology to better understand how individuals of varying WMC and expertise study neuroanatomical images. We aim to: 1) assess differences in gaze patterns between students of high and low WMC when viewing high and low contrast brain slices and 2) compare novices and experts to uncover potential competency‐related differences. Methods Undergraduate students with no prior anatomical education (n=120) and neuroanatomical experts were recruited. During an eye tracking session, participants were given 5 minutes to study twelve structures on digital images of 4 brain slices (either coronal or transverse slices in high or low contrast) and were then tested on their ability to identify the learned structures on similar low‐contrast images. This procedure was repeated such that all participants were exposed to a set of coronal/transverse and high/low contrast images in a randomized fashion. All participants’ eye tracking data and test accuracy were recorded. After testing, participants completed the Automated Operation Span Task (OSPAN) to quantify WMC. Results Preliminary results show that students with high WMC are more accurate in identifying neuroanatomical structures when compared to low WMC students [ F (1, 32), p =.088]. Dwell time was a significant predicting factor for accuracy [ r (32)=40, p =.02]. High WMC students fixated longer on neuroanatomical structures when compared to low WMC students [ F (1, 32)=6.50, p =.02]. Data collection from expert participants is ongoing. Discussion/Conclusion Overall, these results offer a quantitative measure of how neuroanatomical information is viewed and learned to gain insight into neurophobia and influence future teaching practices in neuroanatomy.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.257
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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