The Effects of Data-Driven Learning Approach in a Content and Language Integration Learning Classroom: A Study of Economics Subject in a Thai High School
Bibliographic record
Abstract
There are several studies that teach specific content and the English language at the same time using the CLIL approach, but none of them reflect the students' linguistic ability in the given subject throughout the world, especially in Thailand. The Corpus-Based CLIL will be an efficient combination approach that can improve Thai high school students' comprehension of both English and specific content with an emphasis on Economics, as well as their linguistic ability. The purpose of this study is to (1) compare the effects of the Corpus-based CLIL method on the Economics subject and English language learning, and (2) examine how Thai high school students use CLIL and DDL learning processes while dealing with the Corpus-based CLIL method. This study included 40 high school students from the demonstration school. A mixed-methods study was conducted. The students’ reflections, and the teacher's field notes observation were utilized to gather the qualitative data, while the pre-test and post-test were designed to determine the quantitative findings. The study revealed that students using the Corpus-based CLIL method could greatly enhance their understanding of both English and Economics, as well as linguistic features like syntax and collocation in the Economics. Additionally, students could improve their English, Economics, and DDL learning processes. These findings suggest that the Corpus-based CLIL method can effectively improve students' Economics and language abilities simultaneously.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".