Teachers’ Motivating Methods to Support Thai Ninth Grade Students’ Levels of Motivation and Learning in Mathematics Classrooms
Bibliographic record
Abstract
The aims of this qualitative research were to investigate the level of motivation and learning of ninth grade students in mathematics classrooms in Thailand and to reveal how the teachers supported students’ levels of motivation and learning. The participants were 333 students and 12 teachers in 12 mathematics classrooms from four regions of Thailand. The results showed, first, that the students’ levels of learning ranged from low to moderate-high while the levels of motivation were from moderate to high. In addition, most students had intrinsic motivation; however, some students still lacked motivation and were only motivated by external sources. Second, teachers enhanced students’ learning by encouraging them to learn as a whole class the most by using lectures and asking questions. The other top methods were allowing time for self-paced learning and answering, and relying on internal sources of motivation using positive interpersonal and group activities. Most teachers employed both autonomy-supportive and controlling motivating styles in classroom. Even with a variety of motivating methods, the students’ learning did not seem to be supported adequately, especially for students who showed a low level of 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".