Enhancing Active Learning through the Interactive Learning Platform to Improve Thai EFL Students’ English Vocabulary, Grammatical Retention, and Motivation in English Learning
Bibliographic record
Abstract
This study aims to investigate the effects of implementing active learning activities through an interactive learning platform, ClassPoint, to improve the English vocabulary, grammatical retention, and motivation of Thai undergraduate students in the field of hotel and tourism. The one-group pretest-posttest research design was used. The population was 20 third-year undergraduates enrolling in the hospitality program at a university in the central part of Thailand. The convenience sampling technique was used to select the participants. They included 15 students, with the majority at the A1 CEFR level of English proficiency. The instruments used for this study included pre-, post-, and delayed post-English achievement tests to examine their English proficiency. The questionnaire and focus-group interviews were also used as research instruments to elicit their motivation toward learning English. The results showed that the students were able to retain vocabulary, but they did not demonstrate grammatical retention after the intervention. However, there was a significant increase in motivation in English learning. They were more confident in their language skills, and the active learning activities integrated with the ClassPoint application could inspire them to further develop their language proficiency for their personal interests and future careers.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".