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The Assessment of an Online Microscopic Anatomy Laboratory Course

2011· article· en· W3177315779 on OpenAlexaffabout
Michele Barbeau, Kem A. Rogers

Bibliographic record

VenueThe FASEB Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsWestern University
Fundersnot available
KeywordsAttendanceVirtual microscopyCourse (navigation)Medical educationOnline courseSignificant differenceStrengths and weaknessesVideoconferencingDistance educationComputer scienceMultimediaPsychologyMathematics educationMedicineEngineeringPathologyInternal medicine

Abstract

fetched live from OpenAlex

We have developed a senior level histology course with a laboratory component which is offered fully online. This course utilizes virtual microscopy and synchronous videoconferencing software technologies to allow a synchronous lecture and laboratory course. This online course is being compared to a face to face (F2F) course covering the same content. Initial data suggest that there is no significant difference between these methods for overall student outcomes. Closer examination of the components will determine if there is any difference in outcomes for the lab portion of the course. In addition, attendance levels and patterns will be examined for differences between the online and F2F groups. Demographic data collected will include incoming science and math GPA which will be correlated to the overall course grade. Finally, levels of student satisfaction will be compared for the courses. Many studies have compared online to F2F courses however, few were laboratory courses and none were synchronous courses where both the lectures and the laboratories were interactive. Results from this study will help to clarify any strengths or weaknesses of online laboratories. Grant Funding Source: Ontario Graduate Scholarship

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.084
GPT teacher head0.420
Teacher spread0.336 · 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".

Quick stats

Citations0
Published2011
Admission routes2
Has abstractyes

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