MétaCan
Menu
Back to cohort

Physiology educators’ attitudes, experiences and recommendations after an abrupt transition to remote laboratories

2021· article· en· W3167274619 on OpenAlexaffabout
Julia Choate, Nancy Aguilar‐Roca, Elizabeth A. H. Beckett, Sarah J. Etherington, Michelle French, Voula Gaganis, Charlotte Haigh, Derek Anthony Scott, Terrence Sweeney, John Zubek

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurriculumMedical educationCoronavirus disease 2019 (COVID-19)Thematic analysisDistance educationPandemicPsychologyMedicinePedagogySociologyQualitative research

Abstract

fetched live from OpenAlex

The COVID‐19 pandemic has been associated with university lockdowns, forcing physiology educators to pivot laboratories into a remote delivery format. This study documents the experiences of ten physiology educators from Australia, Canada, the U.S. and the U.K. as they rapidly transitioned to remote laboratories in March‐July, 2020. They wrote reflective narratives that explored their experiences and attitudes about virtual laboratories before, during and after the transition to remote delivery. Thematic analysis of the reflections found that before COVID‐19, few of the educators had utilized virtual laboratories, with most believing that virtual laboratories could not replace the in‐person laboratory experience. In response to university lockdowns, the educators transitioned from traditional on‐campus, in‐person laboratories to off‐campus, remote laboratories within a week or less. This transition was mainly achieved by using commercially available online laboratory software, home‐made videos and sample experimental data (collected before COVID‐19). Opportunities associated with the remote transition included new collaborations (local and international), the exploration of unfamiliar technologies and revisiting the laboratory course curriculum and structure. However, the experience also generated challenges including excessive workloads, lack of expertise, disparities in online and workspace access, academic integrity issues, educator/student stress, changes in learning outcomes and a perceived reduction in student engagement (particularly due to the loss of educator‐student and student‐student interactions). Despite these challenges, most of the educators planned on retaining successful aspects of the remote laboratories post‐pandemic, particularly with a blended model of remote and in‐person (on‐campus) laboratories. This study concludes with recommendations and practical strategies for physiology educators as to how they can plan, develop, deliver and assess effective remote laboratories.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.249

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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 designBench or experimental
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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicBiomedical and Engineering EducationFrench-language works237,207