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Synchronous vs. Asynchronous Anatomy Content Delivery during COVID‐19: Comparing Student Perceptions and Impact on Student Performance

2021· article· en· W3170957831 on OpenAlexaffabout
Christopher J. Ramnanan, Gabrielle Di Lorenzo, Selina X. Dong, Victor Pak, Samantha Visva

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsCanadian Network for Innovation in EducationUniversity of Ottawa
Fundersnot available
KeywordsPerceptionThematic analysisCoronavirus disease 2019 (COVID-19)PreferencePsychologyMedical educationAsynchronous communicationMedicineMedical physicsMultimediaComputer sciencePathologyQualitative researchNeuroscienceMathematics

Abstract

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Introduction/Objective While both asynchronous (ASYNCH) and synchronous (SYNCH) approaches have been used for online medical anatomy teaching during the COVID‐19 pandemic, it is unclear how ASYNCH vs. SYNCH approaches are differentially perceived by medical students, or whether these methods have any differential impact on anatomy learning. The purpose of this study was to compare first year (M1) and second year (M2) medical student perceptions and exam performance between ASYNCH and SYNCH‐delivered anatomy content. Materials/Methods University of Ottawa M1 and M2 perceptions were surveyed (with both close‐ and open‐ended items) regarding musculoskeletal (MSK; delivered to M1) and gastrointestinal/reproductive (GI/REPRO; delivered to M2) anatomy content delivery in Fall 2020. In both cohorts, approximately 50% of the sessions were delivered in each of ASYNCH (prerecorded lectures) and SYNCH (real‐time Microsoft Teams lectures) formats. Final examinations were also analyzed, comparing items that related to content from ASYNCH and SYNCH formats, with 50% of exam items tied to each approach. For both M1 and M2 cohorts, both ASYNCH‐ and SYNCH‐related examination components featured image‐based, multiple choice question (MCQ) items, with similar proportions of structure identification items and clinical application items. Results In M1 (n=101; response rate = 62%) and M2 (n=66; response rate = 40%) groups, the percentage of students indicating a preference for ASYNCH, a preference for SYNCH, or no preference regarding content delivery, was 45%, 38%, and 18%, for M1 students, and 48%, 32%, and 20% for M2 students, respectively. Thematic analysis of open‐ended feedback revealed strengths (more engaging formative assessment; being part of a learning community) and limitations (technical issues; time‐inefficient; more difficult to review) of SYNCH delivery. Similarly, commentary also revealed strengths (time efficiency; flexibility/control) and limits (less engaging formative assessment) of ASYNCH delivery. Commentary also revealed that many students were fine with either approach. While some students noted their preferred approach was beneficial to their learning, assessment data revealed no statistical differences in class performance between ASYNCH‐ vs. SYNCH‐delivered content, for either M1 (MSK) or M2 (GI/REPRO) anatomy. Conclusion While medical students may have indicated a slight preference for ASYNCH (vs. SYNCH) anatomy learning, medical students were generally satisfied with both approaches. While open‐ended commentary revealed strengths and limitations regarding both formats, the perceived notion that either SYNCH or ASYNCH was more beneficial to student learning was not supported by assessment data. Significance/Implication This study provides evidence to suggest that both ASYNCH and SYNCH approaches are appropriate for delivering anatomy content to medical students, given unique strengths attributed to each format, and that both approaches lead to similar performance on knowledge assessments. As such, medical anatomy educators should feel confident in choosing either ASYNCH and/or SYNCH formats when delivering their lectures.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.017
GPT teacher head0.275
Teacher spread0.258 · 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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Citations3
Published2021
Admission routes2
Has abstractyes

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