The Development of an Effective ICC Teaching Model Aims to Enhance the Intercultural Competence of EFL Tertiary Students in Thailand
Bibliographic record
Abstract
The goal of this study was to develop a teaching model for intercultural communicative competence (ICC) that would effectively and efficiently improve Thai English as a Foreign Language (EFL) tertiary students in Thailand. The research and development (R&D) strategy was used as the research design for the study (Phusee-Orn, 2021). The study unfolded over three phases. Phase 1 involved the development of the teaching model; Phase 2 focused on the implementation of the teaching model; and Phase 3 involved the evaluation of the teaching model. In the 2023 academic year, students enrolled in the Bachelor of English Education Programme at Nakhon Ratchasima Rajabhat University in Thailand were selected as research pilot study participants and experimental subjects. The research instruments comprised the ICC teaching model and three teaching modules. The data collection and analysis were assessed using three methods: 1. a questionnaire; 2. pre- and post-tests; and 3. in-depth focus group interviews. This study included a combination of qualitative and quantitative data analysis methodologies. The research results demonstrated that the ICC teaching approach had a substantial impact on improving the intercultural competency of Thai EFL tertiary students, who were studying English as a non-native language. The results indicated that students acknowledged the potential of the ICC teaching paradigm to improve their overall intercultural competence. They reached a consensus that teaching modules 1–3 has the capacity to enhance the intercultural competency of Thai tertiary EFL students. The superior quality (x̄ = 4.85) demonstrated an improvement in students' intercultural knowledge.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".