The Efficacy of Culturally Responsive Pedagogy for Low-Proficiency International Students in Online Teaching and Learning
Bibliographic record
Abstract
International students with low academic English proficiency face challenges with reading their course materials and writing assignments. Their challenges are exacerbated during remote learning, as they remain in their home countries, immersed in their home languages, which may be quite distant from academic English. To investigate the effects of culturally responsive pedagogy for international students online, quantitative and qualitative data were collected from a learner-driven, instructor-facilitated (LeD-InF) support program at a large university in southern Ontario. This fully online delivery of the Reading and Writing Excellence (RWE) program was re-envisioned from a long-running co-curricular program that addressed students’ academic English reading, writing, and critical thinking needs. Among eight groups (with the total enrolment of 154) in the Fall 2020 academic term cycle and nine groups (with the total enrolment of 226) in the Winter 2021 academic term cycle of the online RWE program, the intervention groups that were additionally supported with culturally responsive pedagogy had the highest volume of writing output and engagement metrics among all groups. The text data (of student voices and experiences) also reinforces the efficacy of culturally responsive pedagogy in facilitating student experience, constructing identities, promoting learner agency, increasing satisfaction, improving students’ perceptions of learning, and realizing transformative inclusivity.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".