Synthesis and Model Development of Thai Undergraduate Dropout Risk Factors
Bibliographic record
Abstract
This study synthesizes existing research to explore factors affecting student attrition in Thai higher education institutions and develop a causal model for dropout risk. The synthesis uses a mixed-method approach following PRISMA 2020 guidelines, drawing on six years of Thai contextual studies on student attrition, academic intention, commitment, and persistence. Through a systematic review of multiple databases, 21 quantitative or mixed-method studies were identified for inclusion, which yielded 107 items representing 186 occurrences related to student dropout or persistence factors in Thai higher education. These items were grouped into nine clusters: academic integration, attitudinal and behavioral factors, classroom and institutional environment, emotional distress, family support, financial considerations, institutional support, social integration, and student satisfaction. The model synthesizes research findings on student attrition in Thai universities, providing a comprehensive framework for understanding the factors influencing students' persistence and dropout risk. By considering the interplay between these factors, the model aids in developing targeted interventions and informed policy decisions that promote academic success and ensure the long-term efficacy of Thai higher education institutions. The model's application can potentially guide researchers, educators, and policymakers in addressing the challenges students face within the Thai higher education system, ultimately fostering a more supportive and conducive environment for academic achievement.
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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 | 0.031 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".