The Development and Proposal to Incorporate Multi-Integrated Instructional Strategies into Immigrant Chinese Language Classes
Bibliographic record
Abstract
Due to globalization and the formation of the global village, people worldwide interact in multiple languages frequently. Concurrently, there has been an increase in motivation to learn different languages for individual purposes. As well, we see that the importance of language education is also being promoted. Past language methods have merit, such as visual aids, but it is not a good idea nor appropriate for teachers to just follow suit without considering the language system of the learners, the country of origin of the learners, and the various background and culture of the learners. In view of these characteristics of immigrants as adult learners participating in Chinese language classes, which are different from adult learners of other languages, this paper tries to develop multi-integrated instructional strategies, which include supportive strategy, auxiliary strategy and core strategy. These strategies are based on theories of accelerated learning, whole brain learning, and situated learning. I propose that Chinese language teachers use these strategies in classes for immigrants. The strategies developed will have implications for immigrants, teacher educators, language program administrators, and other stakeholders in similar contexts.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".