Students’ Attitudes Towards Learning English Vocabulary Through Collaborative Group Work Versus Individual Work
Bibliographic record
Abstract
This study investigated university students’ attitudes towards learning English vocabulary through collaborative group work (GW) versus individual work (IW) while performing vocabulary-focused tasks following reading comprehension. The second year, non-English major, Taiwanese students (N = 44) worked either in mixed ability groups of 3–4 or alone. The same students were exposed to the two treatments: classroom intervention was conducted with alternating sessions (one-week IW, one-week GW) for 12 weeks with accompanying tests of vocabulary learning. Attitude questionnaires (44 students) were administered before the classroom intervention and again after, together with interviews (24 students). Results showed that students increased in favourable attitude to IW more than to GW over the study period, even though their actual vocabulary learning improved more with GW. Nevertheless, by the end they did report GW as less stressful than IW and as providing better support and enabling more efficient work on the tasks, with greater likelihood of correct information being obtained. The findings suggest that enhancing students’ vocabulary learning through GW was valuable despite student perceptions of it not being unambiguously favourable, and the use of GW in university English classes needs to be encouraged, albeit with continuity of group membership over time.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".