“I Apology no no I Mean I Am Sorry … Please Let Me Explain That First”: Enhancing Communicative Language Competence of Thai University Students Through CEFR-Based Online Intercultural Communication
Bibliographic record
Abstract
During this COVID-19 pandemic, no one can deny the value of online communication. It has saved our lives by preventing us from going outdoors and becoming infected, while also facilitating achievement of various personal and professional goals. Online communication can also assist us with our academic goals, whether it is used to communicate with supervisors or with people from other lingua-cultural backgrounds to practice the language. This form of communication is emphasized in the Common European Framework of Reference for Languages (CEFR) framework, which is one of the most significant guidelines for language teaching and learning throughout the world. The present research has two aims: first, to determine the extent to which CEFR-based online intercultural communication can improve Thai students’ communicative language competence, and second, to investigate Thai students’ attitudes toward CEFR-based online intercultural communication and international volunteers. The results received from 15 fifth-year dual B.A. (Chinese) and B.CM. (Traditional Chinese Medicine) students and international volunteers using various research instruments revealed that Thai students could increase their communicative language competence after participating in CEFR-based intercultural communication activities. This encompassed linguistic, sociolinguistic, and pragmatic abilities that correspond to the CEFR (2001) scales. In terms of attitudes, the study indicated that, because they were more confident in their communicative language ability, they had a positive attitude toward the CEFR-based online intercultural communication activities and their international volunteers. Finally, further research on this topic should include a proper design of online communication activities as well as methods for assessing students’ competency both before and after participation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".