Need Assessment of the Growth Mindset for Enhancing Learning Management of Teachers in the Primary Schools Under the Office of the Basic Education Commission
Bibliographic record
Abstract
The purposes of this article were 1) to study the current condition, the need for developing a growth mindset to strengthen the learning management of primary school teachers, and 2) to study guidelines for developing a growth mindset to strengthen learning management among primary school teachers under the Office of the Basic Education Commission. There were 2 stages of research: Step 1: Study the current conditions. It is necessary to develop a growth mindset to strengthen the learning management of primary school teachers. The sample consisted of 427 primary school teachers in the academic year 2022 derived by multi-stage sampling. Step 2: Study ways to develop a growth mindset to strengthen learning management among primary school teachers under the Office of the Basic Education Commission. The group of informants is qualified by studying the best practices of 5 persons by specific sampling. The research instruments were 1) a 5-level estimation scale questionnaire, 2) a semi-structured interview form, and 3) a 5-level estimation scale development guidelines assessment form. Statistics used to analyze data include means, standard deviations, priority sorting of PNImodified data, and content analysis. The results of the research showed that 1) the overall current condition and the overall aspect are moderate, and the overall desirable conditions are substantial. It was found that the growth mindset strengthens teachers’ learning management. There is a need for development in all 6 areas. In order: Learning efforts, Acceptance of failures, Acceptance of criticism, Learning challenges, Development paradigms, and Motivation for success. 2) Approaches to developing a growth mindset to strengthen the learning management of primary school teachers under the Office of the Basic Education Commission. This can be done by developing all 6 items and 20 approaches. There are development methods that consist of workshops, and learning from other people. Integration in operations, Self-study, Building a professional learning community, Counselor, and Mentoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".