Effects of Integrating Movie-Based Mobile Learning Instruction for Enhancing Thai University Students’ Speaking Skills and Intercultural Communicative Competence
Bibliographic record
Abstract
This research aimed to: 1) investigate the effects of Movie-Based Mobile Learning (MBML) instruction in enhancing Thai EFL university students’ speaking skills; 2) investigate effects of Movie-Based Mobile Learning instruction in enhancing Thai EFL university students’ intercultural communicative competence (ICC); and 3) examine the students’ attitude towards integrating Movie-Based Mobile Learning instruction for enhancing students’ speaking skills and intercultural communicative competence. This study employed a mixed-methods approach and a quasi-experimental study with a non-equivalent pretest-posttest control group design. The participants were the first year university students learning General English in the Faculty of Nursing at a private university in Bangkok. The students in the experimental group were taught through Movie-Based Mobile Learning instruction while the control group was taught following the university-based instruction. Research instruments consisted of MBML instruction with teaching materials, speaking test, the Intercultural Communicative Competence Questionnaire, Technology Acceptance Model (TAM) questionnaire, and the interview. The results revealed that Movie-Based Mobile Learning instruction developed Thai university students’ English speaking skills and Intercultural Communicative Competence, and that the students had positive attitude towards the implementing of MBML instruction in their English classroom. The findings implied that MBML instruction is effective in enhancing EFL students’ speaking skills and intercultural communicative competence. The study suggests that relevant English language education should emphasize the potential of using the English movies and Mobile Learning in EFL curricula and that MBML instruction can be applied to promote learning in other similar EFL contexts.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".