Grades 1-12 Thai Students’ Learning Styles according to Kolb’s Model
Bibliographic record
Abstract
This survey research aims to explore grades 1-12 students’ learning styles according to Kolb’s model. The data was collected from 9,600 students in 120 schools, which located in 20 provinces in six regions of Thailand. The Learning Styles Questionnaire (LSQ) adapted from Kolb’s model of learning styles were sent to the sample by post and 77.5% of them were returned. The respondents were 7,444 students (59.3% female, 40.7% male) aged from 7 to 19 years old. In data analysis, the respondents’ preferred learning styles were categorized into: Concrete Experience (CE), Reflective Observation (RO), Abstract Conceptualization (AC) and Active Experiment (AE). These learning styles were calculated for mean and standard deviation. The relationships between the respondents’ learning styles and their genders, grade levels, school sizes and regions were examined by using the One-way Analysis of Variance and Sheffe multiple comparisons. After that, the combination of learning styles’ scores was plotted and interpreted into four types of learners including Diverging, Accommodating, Assimilating and Converging and counted for their frequencies. The results revealed that the students’ learning styles were significantly different regarding their genders, grade levels, school sizes and regions. That is, the female students, the grade level 1 students and the students from large-size schools significantly had mean scores in CE, RO, AC and AE higher than the male students, the students in other grade levels and the students from small-size and medium-size schools, respectively. However, the regions that schools located did not show a strong pattern of relationship with students’ learning styles. In addition, most of the students preferred to be the Diverging learners, followed by the Accommodating, Assimilating and Converging learners. The implications from these findings were also discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".