Online learning interaction discourse Indonesian for foreign speakers: The role of teachers in speaking turns on the online Indonesian language learning quality
Bibliographic record
Abstract
Indonesians are starting to play a bigger role in international politics. Indonesian was formally acknowledged as an official language of UNESCO by the General Assembly in 2023. Improving the standard of Indonesian language education for non-native speakers (BIPA) is essential to maintaining and improving Indonesia's reputation internationally. As a result, BIPA has become a prominent and fascinating field of study. The goal of this research is to examine speech turns in the discourse of BIPA learning exchanges. Zoom sessions were used at PGRI Semarang University (UPGRIS) to conduct the case study virtually. The study utilised a blend of qualitative and quantitative methodologies, gathering data via interviews and observations conducted in the academic year of 2024. Interviews were conducted to find out more about research participants' nationality, age, academic background, linguistic proficiency, and motivation for learning Indonesian, while observations were used to collect data on speech discourse and its associated interactional components. The study concludes that turn-taking in BIPA interactions improves learning, and speaking chances are a useful tool. Students find BIPA learning more engaging when turn-taking and word count comparisons are varied.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".