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Record W2103579003 · doi:10.3109/0142159x.2011.545844

Preliminary research in the application of integrated learning and teacher-centredness in undergraduate education in China

2011· article· en· W2103579003 on OpenAlexfundno aff
Jian Wang, Qi Zhao

Bibliographic record

VenueMedical Teacher · 2011
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsMedical educationPsychologyMeaning (existential)Reliability (semiconductor)Mathematics educationMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: The General Practice Department of Fudan University recognised that a traditional didactic educational approach will not achieve expected learning outcomes. Therefore, the Department adopts an integrated learning and teacher-centred approach. AIM: To evaluate the effect of introducing integrated learning and a teacher-centred approach to undergraduate medical education in China. METHODS: The concept of integrated learning and the use of a teacher-centred approach was introduced to the General Practice Department of Fudan University's 'Doctor-Patient Communication Skills' undergraduate course. A self-designed questionnaire and a questionnaire used by Fudan University to evaluate the students' satisfaction with their tutors were used to survey 58 medical students. RESULTS: The self-designed questionnaire gave good reliability and validity results. Based on the survey, 88% of the students both enjoyed the course and rated it highly. The students also showed a high degree of satisfaction with their tutors. CONCLUSION: Although the student numbers were low, their comments have indicated that the newly introduced, but optional course has achieved a highly desirable effect amongst the students. We believe that this effect has been created through a change from a didactic approach to teaching to a more student-centred approach and by giving meaning and purpose to the students learning; in effect making teaching and learning an enjoyable and satisfying experience for all.

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)0.005
metaresearch head score (Gemma)0.003
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 score0.359
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.048
GPT teacher head0.399
Teacher spread0.351 · 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

Citations7
Published2011
Admission routes1
Has abstractyes

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