Teacher’s cross-cultural understanding of student engagement in classroom discussion
Bibliographic record
Abstract
Student engagement has long been an important topic in the research of curriculum. In China, how to improve student engagement in classroom discussion has constantly been a challenge for teachers of English as a Foreign Language (EFL) in universities. Similarly, many Chinese young people who pursue further studies abroad are not active enough in classroom discussion. Why are some students silent in classroom discussion? How does this silence affect their learning, or does it? How can classroom discussion really benefit them? This study participates in the discussion through exploring a specific teacher’s cross-cultural understanding of the phenomenon, adopting narrative inquiry as the research methodology. The field texts (data) include the teacher’s journals and photographs from 2014 to 2018. The teacher’s understanding of Chinese students’ engagement in classroom discussion is continually broadened and shifting, through thinking narratively with her cross-cultural experiences. Her ongoing understandings are as follows: first, silence does not equal non-participation; second, some tend to learn through image more than through sound; third, there is a cultural difference between East and West in classroom discussion; last, the significance and rules of classroom discussion are not self-evident for students.
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.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".