Unveiling beliefs and practices in Chinese vocabulary teaching: a sequential exploratory mixed-methods study
Bibliographic record
Abstract
Abstract Vocabulary is an essential aspect of first-language (L1) teaching and learning. However, little research has previously investigated teachers’ beliefs about and practices of vocabulary teaching in the L1 Chinese context. Accordingly, this study does so using a sequential exploratory mixed-methods approach. The first of its two phases involved qualitative data collection through interviews, observations, stimulated-recall interviews, and assessment of documents. Based on these data, questionnaires were developed for Phase 2: a quantitative study involving 337 teachers aimed at systematically capturing the above-mentioned beliefs and practices. The findings indicate that the sampled teachers primarily focused on meaning-oriented vocabulary teaching, and were more likely to use interactionist than behaviorist teaching methods. Their use of educational technology was extensive, but peripheral, and they placed little emphasis on self-regulated learning. These findings can usefully inform curriculum alignment, and form the basis of our recommendations for effective instructional strategies, including interactive teaching and technology integration. We also propose a framework that facilitates comprehensive analysis of vocabulary teachers’ beliefs and practices while addressing gaps in the L1 Chinese-teaching literature, notably by emphasizing context-specific approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".