Factors Affecting Academic Achievement in Thai Educational Contexts: A Systematic Review
Bibliographic record
Abstract
Academic achievement in Thai educational contexts faces ongoing challenges despite extensive research efforts. This systematic review addresses critical gaps in understanding factors affecting academic achievement by synthesizing findings from multiple Thai studies to provide evidence-based insights for educational practice. Following PRISMA methodology, this systematic review analyzed 214 research studies from an initial pool of 10,249 articles identified through Google Scholar using Publish or Perish software. Studies conducted between 1987-2024 examining factors influencing academic achievement in Thai educational contexts were systematically reviewed using inclusion criteria requiring correlational research with available correlation coefficients. Five core factors emerged as most influential in academic achievement: Learning Attitude (43.93% frequency), Achievement Motivation (40.65%), Teaching Quality (35.51%), Prior Knowledge (31.78%), and Classroom Climate (28.97%). These factors demonstrate both direct and indirect relationships with academic outcomes, operating through complex networks of influence that combine individual characteristics with environmental conditions. Academic achievement in Thai contexts is predominantly influenced by affective factors (attitude and motivation) supported by cognitive foundations (prior knowledge) and environmental facilitators (teaching quality and classroom climate). The findings provide evidence-based guidance for educational interventions while highlighting the importance of cultural adaptation in applying universal educational theories to Thai educational settings.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".