Digital Frontiers: Investigating the Impact of Online Teaching Engagement on Thai Teachers’ Self-Efficacy and Burnout amid the Covid-19 Pandemic
Bibliographic record
Abstract
This study aims to investigate experienced Thai teachers’ experiences of burnout during online teaching and learning, and examine how teachers’ self-efficacy and burnout levels impact their teaching performance. The research concerns differences in perceptions of emotional exhaustion, depersonalization, personal accomplishment/assessment, teachers’ self-efficacy, and online teaching performance among teachers in Thailand. The sample selection was conducted using a stratified random sampling technique. Data collection involved self-reported surveys from 243 elementary, secondary, and vocational schoolteachers in metropolitan areas and Thailand’s north, northeast, east, and south regions. MANOVA and correlation analysis were employed to analyze burnout, teachers’ self-efficacy, and teaching performance. The results indicated differences in online teaching performance and teachers’ self-efficacy between two groups: high-risk and moderate-to-low risk of burnout. Teachers with a low risk of burnout demonstrated higher self-efficacy and better performance during online teaching. The study identified two burnout subscales—emotional exhaustion and depersonalization—originally included in the Maslach’s burnout inventory. However, we also incorporated teachers’ online teaching performance into the assessment, necessitating modifying the Maslach Burnout Inventory. Regarding implications, we recommend practical applications in policy improvements related to teachers’ mental support and reducing burnout causes while enhancing online teaching performance.
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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 | 0.007 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".