Digital Storytelling and Intercultural Communicative Competence through English as a Foreign Language for Multilingual Learners
Bibliographic record
Abstract
In the age of rapid growth in technology, language education policies in the education system must proceed with a progressive and inclusive vision. Intercultural communicative competence (ICC) is also integral for language learners to avoid miscommunication and become knowledgeable of individuals with a myriad of ethnicities and beliefs. This study aims to examine the method of incorporating ICC with digital storytelling in English as a foreign language (EFL) class to promote inclusivity among multilingual students through an extensive literature review and reflection on English teaching in Thailand. The components and assessment of ICC, as well as the implementation of digital storytelling will also be discussed. Learning through multimedia production and intercultural stories can enhance the English skills of multilingual students when the teaching methodology is aligned with the geographical and cultural bedrock of language learners. Project-based instruction such as interviews, culture logs, and presentations representing both Western and local cultures are recommended to evaluate the English speaking skills of students.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".