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The Anatomy of Traditional and E‐Learning Education: How the spatial ability of learners can impact learning outcomes

2017· article· en· W4389023847 on OpenAlexaffabout
Sonya E. Van Nuland, Kem A. Rogers

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsKinesthetic learningSpatial abilityTask (project management)CognitionPerspective (graphical)PsychologyComputer scienceMultimediaMathematics educationArtificial intelligenceNeuroscienceEngineering

Abstract

fetched live from OpenAlex

Technological innovation is changing the landscape of higher education, and the competing interests and responsibilities of today's learners have propelled the movement of post‐secondary courses into the online environment. In the anatomical sciences, computer‐aided instruction and online learning tools have become a critical component of teaching the intricacies of the human body when physical classroom space and cadaveric resources are limited. Our previous research (n=70) compared a simple 2‐dimensional e‐learning tool (A.D.A.M. Interactive Anatomy) to a more complex tool that allows for a more 3‐dimensional perspective (Netter's 3D Interactive Anatomy). Despite the differences in how these e‐learning tools present information, student ability to learn anatomical material, and their mental effort while doing so, known as cognitive load, were identical between e‐learning tools. However, when students with low spatial ability studied anatomical content with the more complex tool (Netter's 3D Interactive Anatomy), their performance scores were significantly lower than those students with high spatial ability (p=0.007). These results indicate that e‐learning tool software design can differentially influence students based on their spatial ability, but it remains to be determined if traditional kinesthetic‐tactile learning approaches, such as manipulating a skeleton, are also impacted by a student's spatial ability. Using a novel dual‐task methodology with a cross over design, undergraduate anatomy students from The University of Western Ontario, Canada (n=71) were evaluated as they studied a bony joint using a physical skeleton as well as a simple commercial software program (A.D.A.M. Interactive Anatomy). We hypothesized that the acquisition of anatomical knowledge by students, regardless of their spatial ability, would be superior when learning is associated with a real model, rather than currently available e‐learning tools. Students were assessed using a baseline knowledge test, Stroop observation task response times (a measure of cognitive load), MRT scores (a measure of spatial ability) and an anatomy post‐test (a measure of learning). Results suggested that while students may experience more cognitive load while studying using a physical skeleton (p<0.001), it does not detrimentally impact their performance; in fact student performance was significantly higher when they studied using the skeleton (p<0.001, R=0.46). Furthermore our results also demonstrated that students with low spatial ability are at a significant disadvantage when they learn the bony anatomy of a joint and are tested on images of the contralateral joint (p=0.023, R=0.326). This study highlights a major weakness in the strategy to move traditional anatomical education online, and suggests that students should be taught the anatomy of both sides of the human body, regardless of the reality that human limbs are mirror images of each other. These results can be further applied to the training of future surgeons and medical specialists, where surgical and medical procedures should be taught and practiced on both sides of the human body, to ensure that all students, regardless of spatial ability, can take their anatomical knowledge into the clinic and perform successfully. Support or Funding Information Social Science and Humanities Research Council, Government of Canada

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.016
GPT teacher head0.273
Teacher spread0.257 · 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 designObservational
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
Published2017
Admission routes2
Has abstractyes

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