Students’ Motivation and Learning and Teachers’ Motivational Strategies in English Classrooms in Thailand
Bibliographic record
Abstract
This research aimed to investigate second language learners’ motivation and learning of English and the ways in which the teachers supported the students’ motivation and learning in natural classroom settings. Based on Self-Determination Theory (SDT), questionnaires were developed and data were collected from students and their teachers in twelve English language classrooms around Thailand. In addition, each lesson was observed by two observers. The data were triangulated and used to describe the students’ motivation and learning and the teacher’s motivational strategies in each class. The findings showed that most students had a relatively high level of motivation and many reported having internal interests in learning English; however, the level of learning was not assessed to be as high. Furthermore, a few students in almost every class showed a lack of motivation. The teachers were found to employ a variety of motivational strategies, including autonomy-support and controlling styles. While autonomy controlling strategies were commonly used in these classes, autonomy-support strategies were found only in highly motivated and high performing classrooms. The findings from this study suggest the use of strategies that do not only initialize but also nurture students’ internal motivation in order to enhance sustainable learning of English in and outside the classroom; therefore, research on how motivation theories are deployed in teacher education programs should be further undertaken.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".