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Record W4310978616 · doi:10.5539/elt.v15n12p98

On the Problems and Solutions of Professional Ethics Education of Normal Students from Educational Narrative Research

2022· article· en· W4310978616 on OpenAlexvenueno aff
Yanfang Zhou, Yinyi Huang

Bibliographic record

VenueEnglish Language Teaching · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIdeological and Political Education
Canadian institutionsnot available
Fundersnot available
KeywordsConnotationNarrativeClass (philosophy)PsychologyProfessional ethicsPedagogyMathematics educationSociologyEngineering ethicsEpistemology

Abstract

fetched live from OpenAlex

Professional ethics is the core quality of teachers. Teachers' professional ethics education for normal students is a problem that society, universities and normal students must attach great importance. This paper uses narrative research to study the professional ethics education of outstanding normal students. Firstly, it expounds the connotation of narrative research. Secondly, in the form of interviews and narrations, the paper selects five normal students of 5 different majors from 4 different universities and named respectively A, B, C, D and E as the objects, to investigate the current situation of professional ethics education for college normal students, and to sort out some existing problems. Further, we delivered the questionnaires to broaden the scope of research. It is found that the way of professional ethics education for normal university students is single, mainly in class; the contents are boring, lacking of innovation; some normal students' consciousness of professional ethics is weak. So the effect of education needs to be improved urgently. The causes of these existing problems are also analyzed further. And some relative solutions have been proposed. It is expected to promote the development of professional ethics education for outstanding normal students.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.094
GPT teacher head0.460
Teacher spread0.366 · 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.

Study designQualitative
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
Published2022
Admission routes1
Has abstractyes

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