The transformative role of E-readers in inclusive online Mandarin education: A duo-ethnographic study
Bibliographic record
Abstract
Many Mandarin programs in Canada rely on prescriptive, textbook-driven curricula that may overlook the diverse learning needs and backgrounds of plurilingual students. This research seeks to challenge and reimagine this traditional curriculum by exploring how Mandarin educators can integrate E-readers as alternative materials to address students’ emerging learning needs. In the post-pandemic era, online language education has become the new normal. While extensive insights have emerged from online English language teaching regarding its challenges, benefits, and implications, the specific context of online Mandarin education remains insufficiently explored, particularly in North America. Given the growing demand for Mandarin language learning in North America, this study engaged two Mandarin teachers in Canada in a 12-week series of online sessions utilizing E-readers (i.e., iChineseReader) as core teaching materials. Employing a duo-ethnographic approach, this research collected data: video-recorded weekly critical dialogues, reflective journals, and lesson plans as pedagogical artifacts. Narrative inquiry guided data analysis, focusing on the teachers’ evolving ideologies surrounding E-reader integration in online Mandarin classrooms. This study presents key stories in terms of: (1) E-readers as a support for ethics-oriented curricula; (2) E-readers as mediators of learner autonomy; and (3) E-readers as tools for plurilingual, multimodal, and identity-affirming learning. This research invites educators to creatively use E-readers to enhance inclusivity, foster learner agency, and recognize students’ linguistic and semiotic resources in online Mandarin learning.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".