Teaching Hanzi Using Correct Stroke Order and Bujian: An Analysis of CEGEP Students' Learning Experiences
Bibliographic record
Abstract
Learning hanzi (Chinese characters) has been regarded as a challenging task due to the complex strokes, the rupture between shape and sound, and the memorization required. Targeting a Chinese as a Foreign Language (CFL) student audience, this paper demonstrates the pedagogical benefits of learning the correct Chinese order of strokes (COS) and bujian (component) for hanzi acquisition. This research was conducted at a CEGEP (Collge d'enseignement gnral et professionnel in French; General and Vocational College in Quebec in English) located in metropolitan Montreal. Results showed that students' knowledge of COS and bujian improves the outcome of their handwriting. When writing hanzi without first being demonstrated COS, students tended to make mistakes in strokes, shapes or structure, such as an extra hook or an asymmetrical appearance. However, after being instructed the correct COS, the mistakes decreased. Moreover, it is noticeable that the effects of COS interweaved with students' previous knowledge of bujian. When students wrote new hanzi that were comprised of bujian that they had been previously exposed to, they often wrote correctly, with appropriate shapes and space arrangements. Students' surveys further affirmed their appreciation of COS and their preference of an instructor's in-person guidance while taking advantage of multimedia teaching tools for assistance. Following these findings, this paper analyzes several useful pedagogical approaches, including the phenomenographic teaching approach, that allow instructors to prioritize learners' perceptual experiences through engaging and proactive learning processes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".