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Record W1995533905 · doi:10.3138/jvme.38.4.414

An Experimental Teaching-Learning Program in Histology

2011· article· en· W1995533905 on OpenAlexvenueno aff
Enzhong Li, Yanfang Chen

Bibliographic record

VenueJournal of Veterinary Medical Education · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSection (typography)Style (visual arts)Mathematics educationMedical educationPsychologyTeaching methodMedicineComputer scienceArtVisual arts

Abstract

fetched live from OpenAlex

This article presents an experimental teaching and learning program used in a histology course with first-year students in the first term of their studies in the Faculty of Veterinary Medicine at Huanghuai University, China. Eighty students were divided randomly into two sections (n=40/section). Students in one section were taught using a hybrid case-based and lecture style (experimental section) and the second section was taught using a traditional teaching style (control section). To assess achievement, examinations were administered at the end of every textbook chapter and a final examination was carried out at the end of the course. To determine students' attitude to the teaching styles used, a questionnaire survey was conducted at the end of the term. Results showed that students preferred the experimental style. In the experimental section, students' scores were significantly higher than those of students in the control section both in the chapter and the final examinations (p<.05). Results indicate that the case-based and lecture style is superior to the traditional style of teaching and learning histology.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.097
GPT teacher head0.456
Teacher spread0.359 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2011
Admission routes1
Has abstractyes

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