A study of Chinese students' academic listening needs for academic success in Canadian universities /
Bibliographic record
Abstract
With English becoming a world language, an increasing number of non-native-English-speaking (NNES) students are pursuing studies in English-medium universities. Of these NNES students, Chinese students constitute a large proportion. Most of these Chinese students are NNES and need English language support to help them meet academic demands in English-speaking settings. However, there are a very limited number of studies conducted on linguistic needs and deficiencies among Chinese students at English-speaking universities in Canada. The main objective of this thesis is to discover Chinese students' perceptions of academic English listening competence and to investigate their academic listening needs for academic success at Canadian universities. This small-scale study at two Canadian universities, conducted through a questionnaire survey and follow-up interviews, fills a gap in the limited number of studies concerning Chinese students' language-development needs at Canadian universities. Findings of this study support the following points. Firstly, Chinese students think that having sufficient English academic listening competence is crucial and necessary for academic success in academic English settings. Secondly, Chinese students still have difficulties in various academic listening skills, and factors that affect students' listening comprehension are both linguistically and socio-culturally related to the new settings. Thirdly, Chinese students still need target-language linguistic support even though they are admitted into English-medium universities. Finally, apart from academic listening competence, Chinese students report deficiencies in academic writing, reading and speaking as well. In addition, this study also suggests that Chinese students may lack good strategies for enhancing their English-language proficiency.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".