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Does Online Anatomy Course Delivery Improve Visuospatial Ability in Novice Learners?

2022· article· en· W4225326864 on OpenAlexaffabout
Kaitlin Marshall, Kristina Lisk, Judi Laprade

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsHumber PolytechnicUniversity of Toronto
Fundersnot available
KeywordsPsychologySpatial abilityAnatomyMedicineCognitionNeuroscience

Abstract

fetched live from OpenAlex

Introduction The study of anatomy can be challenging for students, as complex anatomical and spatial relationships can be difficult to master, particularly in virtual learning environments. Having good visuospatial ability (VSA) can be important for students to have in order to help them understand complex three‐dimensional relationships. There is evidence that learning anatomy in‐person can improve an individual's VSA and that those with higher VSA perform better in anatomy courses. However, no research has explored what effect learning anatomy in a completely virtual environment has on VSA. Thus, the first aim of this study is to determine if there are changes in VSA with online anatomy instruction in novice learners over one semester, and second, to examine if those with higher VSA interact differently with online resources. For our first aim, we hypothesize there will be an overall improvement in VSA over the course of the semester. Our second aim will be analyzed at a later date. Methods First‐year undergraduate anatomy students at the University of Toronto were recruited for this study (n = 253). At the beginning of the semester in September 2021, before learning anatomical content, participants completed the redrawn Vandenberg and Kuse Mental Rotations Test (MRT‐A) which consisted of 24 questions. For the MRT‐A, participants were presented with a target figure and four stimulus figures, where two were a match to the target figure, but had been rotated. If both stimulus figures chosen were correct, one point was rewarded, for a total of 24 points and the full scores calculated out of 100 (24/24). In December 2021, another MRT‐A test will be administered, along with an online questionnaire that will ask students about what type of resources they used throughout the semester and how often they were used. In January 2022, focus groups will be conducted to gain a more in‐depth understanding into which learning tools students found most useful and why, along with how they were being used. Results Preliminary results indicate an initial mean score of 35.20 ± 17.54 with a median of 33.33. Students that scored below the median were categorized as low VSA (n = 112) and those who scored above were categorized as high VSA (n = 140). Additionally, the mean of students that identify as male had a mean of 40.55 ± 18.14 (high VSA = 83; low VSA = 39), those who identify as female had a mean of 30.03 ± 29.17 (high VSA = 55; low VSA = 70), and those who identify as non‐binary or chose “Prefer not to say” had a mean of 31.25 ± 17.14 (high VSA = 2; low VSA = 2). There was a significant difference between the mean score for males and females (p<0.001), but not for any other comparison. Conclusions Initial results appear to be in line with previous research suggesting that males have higher VSA than females. Future analyses will provide a more in‐depth evaluation of overall changes in VSA, gender VSA changes, and the type of resources students engaged with during their online anatomy course. This research may be used as a baseline for further studies exploring course delivery in‐person, mixed, and remote to help instructors create equitable learning environments for students with differing VSA. Additionally, this research will help inform the development of an anatomy learning toolkit that will be used to help leaners develop better spatial reasoning skills.

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)0.001
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 score0.852
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.236
Teacher spread0.231 · 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
Published2022
Admission routes2
Has abstractyes

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