Do Group Exams Support English as an Additional Language Student Learning?
Bibliographic record
Abstract
Abstract Team-based learning (TBL) has been shown to improve many aspects of student learning, but no previous research has systematically examined the effects of group exams on English as an Additional Language (EAL) students in university classrooms. This study is a small-scale action research examining the role of students’ English language status in their perceptions of and performance in group exams within TBL. The data were collected from 29 students – (13 EAL) and 16 English as a first language (EL1) – attending a third-year university linguistics class in Vancouver, Canada. Qualitative and quantitative methods were used for the data analysis. Results of the study revealed no statistically significant differences between the two groups in terms of perceptions or performance. Both groups had positive perceptions about group exams and the increase in their performance over the course of the semester was statistically significant. Qualitative results showed that students found group exams helpful for learning and reducing exam anxiety. Students also enjoyed the experience of taking group exams and stated that their attitudes toward the group exams were more positive at the end of the semester.
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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 | 0.003 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".