Promoting University Students’ Collaborative Learning through Instructor-guided Writing Groups
Bibliographic record
Abstract
This paper aims to examine how to promote university students’ engagement in learning by means of instructor-initiated EFL writing groups. The research took place in Rwanda and was undertaken as a case study involving 34 second year undergraduate students, divided into 12 small working groups and one instructor. The data were collected by means of open-ended group interviews carried out after each of the 12 groups had finished writing an essay in English. In their responses, students acknowledged having improved their interpersonal and collaborative skills through EFL group writing. Students also indicated that, while discussing and interacting with their group members and with the support from their instructor, they improved their English vocabulary, gained new ideas and perspectives, and learned better about text coherence, which led to the improvement of their EFL writing. However, a small section of students did not appreciate writing together due to continued internal disagreements and member incompatibility. Some strategies are proposed to make group work an effective learning tool in and outside the classroom, particularly in EFL contexts.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".