Motivations, beliefs, and Chinese language learning: a phenomenological study in a Canadian university
Bibliographic record
Abstract
Internationally, more and more people are learning Chinese as a second or foreign language. Many studies (Gardner, 1958; Spolsky, 1969; Dörnyei, 1994; Oxford & Shearin, 1996; Williams & Burden, 1997) have shown that learning motivation plays an important role in language learning, while language belief (Horwitz, 1988) determines the strategies and efforts learners are going to put into language learning. Both motivation and belief are key factors in successful language learning. This research carried out an investigation of the phenomenon of Chinese language learning in the Canadian context. Through in-depth, open-ended individual interviews with six students who were learning Chinese in a Canadian university, the researcher intended to listen to their actual experiences of Chinese language learning in order to examine their motivations for learning this language and to describe their beliefs about this language. The results showed Chinese language learners had a variety of motivations to learn the Chinese language, from cultural interest, communication with native Chinese speakers, travel, friendship, to job opportunities. These motivations came from their real life experiences with the Chinese people around them. As for the Chinese language, not all students thought it was difficult. All participants in this study believed listening and speaking was more important than reading and writing. They adopted many learning strategies to learn Chinese. The implications for Chinese language instructors as to how to motivate students and for the Chinese language students motivating themselves were also discussed.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.049 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".