Mitigating Undergraduate Learning Burnout: Development of an Assessment Tool and Positive Thinking Training Program
Bibliographic record
Abstract
The purposes of the current study were to 1) develop a learning burnout assessment for Thai undergraduate students and 2) examine the effects of a positive thinking training program on Thai undergraduate students’ learning burnout. The study was divided into two parts: the instrumental development of the assessment tool and the implementation of the positive thinking training program. The first part involved 250 undergraduate students selected using a multi-stage sampling method. The second part involved implementing the positive thinking training program with 25 participants. The results led to the creation of a learning burnout assessment tool for Thai undergraduate students, encompassing the components of emotional exhaustion, social disengagement, and academic workload. The assessment tool demonstrated content validity, construct validity, and reliability. Additionally, the positive thinking training program effectively reduced learning burnout among participants. This study contributes to the field by introducing a validated learning burnout assessment tool in the Thai educational context and demonstrating the benefits of psychological training in reducing learning burnout.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".