Lecturer’s Working Environment and Teaching Competence in Selected Agricultural Colleges in Vietnam
Bibliographic record
Abstract
The paper entitled Lecturer’s working environment and teaching competence in selected agricultural colleges in Vietnam conducted during the period from October 2013 to March 2016. This section demonstrates how the target and objectives of the study have been achieved. Moreover, answers to the research questions are provided. The study was conducted in 12 colleges, under the Ministry of Agriculture and Rural Development, with agricultural major in Vietnam, the total sample of 300 leaders, managing officers and lecturers. Data analysis using SPSS 16.0 software.Defined seven criteria for assessinglecturer’s teaching competence in agricultural colleges in Vietnam consist of: i) Professional competence and broad understanding; ii) Competence on understanding students during teaching process; iii) Competence on lesson composing; iv) Teaching organizing competence; v) Evaluating and criticizing competence; vi) Competence for communicating, negotiating and making decisions; and vii) Competence for learning and self-developing. With significance level of 5 %, The factors of working environment are listed in descending order of their influence on the development of lecturer’s teaching competence as follows: i) Working conditions; ii) Lecturer’s relationships; iii) Study and promotion opportunities of lecturers; iv) Working pressure and managing environment; v) Students. Within these factors, the factor working conditions is dependent on financial investment, whereas others depend less on financial investment but more on time investment and support from the leaders and staff of lecturers to improve the cultural-psychological environment; at the same time it is necessary for colleges to build reasonable schemes and policies towards lecturers. Results of this research are important and reliable scientific materials, on which colleges can apply to enhance the lecturer’s working environment, contributing to helping them develop their teaching competence and improving the quality of teaching. This research also serves as the scientific foundation for future research in relevant fields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".