How language proficiency contributes to Chinese students’ academic success in Korean universities
Bibliographic record
Abstract
The present study investigated the key determinants of Chinese students’ academic success in terms of GPA and the number of credit hours earned in Korean Universities. The determinants investigated included gender, age, prior academic performance, academic self-efficacy, the TOPIK score, self-perceived Korean and English proficiency, and the previous length of Korean and English study. This study specifically focused on three research questions concerning the prediction of Chinese students’ academic success in Korean universities, the additional contribution of Korean and English language proficiency, and the examination of prediction patterns for undergraduate and graduate students. A questionnaire was issued and collected from 138 undergraduate and 63 graduate Chinese students studying in 27 different Korean universities. The questionnaire consisted of four sections: demographic information, academic background, language proficiency and psychological factors. Correlation and multiple regression analyses were conducted to address the proposed research questions. The findings demonstrated that traditional factors, including gender and prior academic performance, were effective predictors of academic success. However, academic self-efficacy did not play an influential role in participants’ academic success. Language proficiency had a moderate effect on Chinese students’ academic success, which is consistent with previous studies that reported a positive statistically significant relationship between language proficiency and academic success. The different natures of undergraduate and graduate studies determined that the predictors of undergraduate and graduate students’ academic success were different. The present study addressed the research gap by integrating theoretical constructs from both psychology and language education, and also by exploring the relationships between language proficiency and academic success in a less researched test, TOPIK, and in two second languages, Korean and English, at the same time. The findings of this study contribute to the overall understanding of international students’ academic success, in particular the success of Chinese students studying in Korean universities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".