The Effects of Blended Learning on the Intrinsic Motivation of Thai EFL Students
Bibliographic record
Abstract
The aim of this study is to compare the results of blended learning with face-to-face learning among university students studying English as a foreign language. The participants were separated by gender, and the following variables, intrinsic motivation for learning English, attitudes towards English as a subject, and satisfaction with the learning climate, which was either a blended learning environment or a face-to-face learning environment, were analysed. The participants of this research are bachelor’s degree students enrolled in English courses. The two class samples were drawn at random. The first class was the control group, and the other was the experimental group. The experimental group was taught using blended learning, and the control group was taught using face-to-face learning. The results of the research did not differ by gender. The students had significantly higher levels of intrinsic motivation for learning English, a better attitude towards English as a subject, and better satisfaction with the learning climate after they were taught by blended learning. Finally, the students who were taught using blended learning had significantly higher levels of intrinsic motivation for learning English and a better attitude towards English as a subject, as well as greater satisfaction with the learning climate than the students who were taught using face-to-face learning.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".