A Scoping Review of the Research on the Teaching Models of Online International Chinese Language Teaching
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The realm of online international Chinese language teaching is undergoing significant transformations propelled by the internet and the pandemic. The digital teaching is the way forward for online international Chinese language teaching. There are significant differences between online international Chinese language teaching and traditional Chinese language teaching. To improve online teaching activities and guide future research, this growing field aims to analyze Chinese international online education research. Specifically, the goal is to develop a sustainable teaching model. By utilizing Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P), the authors analyzed 27 articles proposing a viable teaching model. The review identifies six models of online Chinese language teaching: flipped classroom, APP software, open online platform, online interaction, blended learning, and ChatGPT. The blended learning model is considered the most effective but still faces limitations such as technical issues, demanding teachers, lack of motivation, limited face-to-face interaction, and limited assessment options. Scholars suggest solutions including technical support, teacher training, motivation, improving face-to-face interaction, and using multiple assessment methods. Overall, the blended teaching model has the potential to advance online international Chinese language teaching in the future.
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.
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 | 0.005 | 0.003 |
| 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 | 0.002 |
| 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 it