Has the Implementation of MLE Improved the Achievements in Thailand’s Deep South?
Bibliographic record
Abstract
Long-term evaluations of student performance are important to show whether Multilingual Education (MLE) students are making real progress, as well as to show what changes are needed to make MLE programs more successful. The purpose of this paper is to describe a six-year study of MLE students in Southern Thailand. .In 2007, the Research Institute for Languages and Cultures of Asia, Mahidol University (RILCA-MU) initiated the Patani Malay-Thai Bi/multilingual Research Project in four schools in Southern Thailand. In 2011, the Yala Rajabhat University (YRU) staff began biannual student evaluations of both the experimental (MLE) schools and the “normal” Thai-only comparison schools, when the first cohort of students began primary grade 1. YRU followed these students’ performances until 2016 when they completed primary grade 6. The learning achievement for students in the experimental (MLE) schools was found to be significantly higher than that of students in the comparison schools at the level of 0.01, except in grade 6. The number of students who met the basic educational criteria was greater for the MLE schools than the comparison schools. MLE was found to be very helpful for low and mid-level performing student. Finally, scores on the critical thinking skills assessment of the MLE students were greater than the comparison school students. Thus, this six-year research project has clearly shown that MLE can help to solve the problems of teaching and learning in Thailand’s three southern border provinces. This approach to long-term evaluations can be helpful to projects in other countries also.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".