Curriculum Evaluation: Measuring the Learning Outcomes and Satisfaction Levels of Thai Adult Learners with an English for Cultural Tourism Communication Course, Suphan Buri, Thailand
Bibliographic record
Abstract
A curriculum on English for cultural tourism communication was designed, developed, and implemented for Thai adult learners in Doem Bang Nang Buat, Suphan Buri, based on adaptation of the grassroots model (Taba, 1962) and relevant research. This study focused on measurement of the learning outcomes and satisfaction levels of Thai adult learners with an English for Cultural Tourism Communication course. Participants were 21 adult learners living in the community of Doem Bang Nang Buat in Suphan Buri. Data were collected through pretests and posttests, a questionnaire, and participant observation, and analyzed using descriptive statistics such as mean and standard deviation. Content analysis was also applied. The results indicated that the learning outcomes of the adult learners improved significantly as the posttest mean scores were higher than the pretest mean scores at the statistical significance level (p < .05). The satisfaction levels of adult learners with the course were rated overall as very satisfied regarding teaching competencies, materials and methods, activities, learning facilitation, and knowledge and understanding of lessons. The study suggests that an English language development curriculum for adult learners should concentrate on learner needs and interests with the aim of presenting everyday English situations in an effort to enable learners to apply English language knowledge and skills to their professions. Furthermore, collaboration between native and non-native English speakers along with the utilization of technology in a positive learning environment is seen as necessary to enhance adult 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 | 0.004 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".