Collaborative learning in a Japanese as a foreign language classroom
Bibliographic record
Abstract
This study examined the influence of training on Asian learners' beliefs, interaction, and attitudes during collaborative learning (CL) and explored the processes of their CL in pairs. The literature contains few studies on the effect of collaborative training in language learning. In addition, it shows gaps between SLA theory and practice resulting from learners' cultural differences. Although second/subsequent language acquisition (SLA) theory assumes that CL contributes to language learning, implementing CL in a multicultural classroom is often considered to be unsuccessful by teachers. The research questions designed to address this gap explore: (a) the extent to which tra~ng affects Asian learners' attitudes towards and interaction during CL; (b) how Asian learners accomplish collaborative tasks in pairs. In the quasi-experimental research design, the learners in the treatment group received special training in CL for 5 weeks while the learners in the comparison group did not receive similar training. Data were collected from 45 McMaster University students through pre- and posttests, pre- and postintervention questionnaires, student information, and informal classroom observations. To detennine the influence of training, the frequency of communication units (c-units), Language Related Episodes (LREs), Collaborative Dialogue (CD) from audio-taped data, and the fmal draft scores were compared between pre- and posttests. The learners' pre- and postintervention questionnaires were also compared. Transcripts from audio-taped data, students' information, their responses and comments from questionnaires, and informal observations served to investigate the processes of Asian learners' CL. Overall, this study found that training had significant influence on the frequency of c-units and CD, and considerable impact on the draft scores, although little influence on the frequency of LREs was observed. The results from the questionnaires in the treatment group showed positive changes in the learners' beliefs on pair work after training. On the other hand, analyses of the transcription data showed that the learners did not conduct enough discussion for a resolution of problems with peers. In conclusion, results suggested the need for teacher intervention, a longer period of collaborative training, and an implementation of self-evaluation into the course grade to encourage the learners to succeed in collaborative learning.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".