E-Writing-Based Pedagogy to Advance Chinese Language Learning: Practices From Heritage and Non-Heritage Teaching at a Major Canadian University
Bibliographic record
Abstract
This chapter presents a successful e-writing pedagogy at the Chinese Language Program at the University of British Columbia (UBC) gradually adopted over the past decades. It showcases the positive outcomes of this teaching reform, resulting in progress among students, satisfaction among teachers, and favorable development of the program, creating a mutually beneficial situation. The innovative approach effectively engages students in meaningful language communication, significantly impacting motivation and achieving the ultimate goal of advancing intercultural communication skills in Chinese. The iterative process has transformed instructor attitudes and practices, reflected in improved outcomes and a consistent upward enrollment trend over a decade. The chapter details diverse e-writing tasks, each with a unique rationale, design, and examples of student work. For character and vocabulary learning, the program combines hand-writing and e-writing to provide an analytical understanding of characters, replacing rote copying. E-writing exercises encourage creative memorization methods, enhancing retention. In tasks targeting various language skills, the program relies predominantly on e-writing for immediate feedback, revision, and collaboration, accelerating learning and boosting language proficiency. E-writing aids heritage stream students in identity exploration and community building, while non-heritage students benefit from a fair learning environment, lowering their learning anxiety and ensuring harmony in all four language skills. Student evaluations confirm e-writing’s effectiveness in facilitating the recognition 168 of Chinese characters, offering more opportunities for authentic language input and efficiently refining integrated language skills. The e-writing pedagogy implemented at UBC, developed through trial and error, aims to serve as a successful model for incorporating e-writing into Chinese language teaching across North American universities. Colleagues are encouraged to participate in this exploration, with the confidence that further discussions and collaboration will contribute to the improvement of effective teaching practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".