Enhancing Teacher Professionalism: A Study of Factor Affecting Self-Development Among Thai Private School Teacher Using MMR Approach
Bibliographic record
Abstract
Amid rapid social change, teacher self-development has become essential. High-quality teaching, exemplary behavior, and professional responsibilities play a crucial role in enhancing student learning. This study aimed to analyze the causal factors influencing self-development needs according to professional teaching standards in private schools and to explore effective self-development approaches.A mixed-methods research design with a FOLLOW-UP Explanatory Sequential Design was employed. The sample comprised 412 private school teachers in Thailand, selected through two-stage random sampling, along with nine key informants. Data were collected using a questionnaire and interview guidelines, and analyzed with LISREL 8.72 and content analysis.The findings indicated that the causal model of self-development needs aligned with empirical data, with attitudinal factors (MIND) exerting the strongest direct influence (0.710). Regarding self-development approaches, teachers should be encouraged to recognize the importance of change, adopt school-based development, follow the PDCR cycle, and implement Professional Learning Communities (PLC). Case studies should support teachers in designing development strategies focused on student reinforcement, managing classrooms in both online and on-site settings, and fostering teacher-parent collaboration. Additionally, both online and offline technologies should be utilized for training to enhance technological proficiency. This study provides essential insights into the self-development needs of private school teachers, highlighting key influencing factors and strategies to support their professional growth.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".