Development of Listening Comprehension and Pronunciation of Pinyin Characters in Mandarin Chinese Through Online Language Teaching: A study on Chinese Language Students at the Faculty of Education, Uttaradit Rajabhat University
Bibliographic record
Abstract
This study explores the effectiveness of online Chinese language courses, focusing on the development of listening comprehension and Mandarin pinyin pronunciation skills among Chinese language students at the Faculty of Education, Uttaradit Rajabhat University. Utilizing a systematic approach, the research employed a pre-test and post-test design to evaluate the impact of the instructional methods on students’ language abilities. Significant improvements were observed in both listening comprehension and pronunciation skills post-intervention, as evidenced by statistical analysis. Moreover, the study gauges students’ satisfaction with the online learning experience, revealing high levels of contentment regarding course content, accessibility, and instructional delivery. The research outcomes have demonstrated a statistically significant improvement in the skills assessed. Students’ scores in listening comprehension and Mandarin pinyin pronunciation substantially increased from pre-instruction to post-instruction. Specifically, the average scores in listening comprehension improved from 16.45 to 24.50, while pronunciation scores rose from 10.85 to 16.30, both evidencing significant advancements with a significance level of 0.05. These results underscore the effectiveness of the online Chinese language courses in enhancing students’ linguistic capabilities.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".