Effects of the Internalization of Peer-Modeled Self-efficacy on Coping with L2 Communication Stress
Bibliographic record
Abstract
This mixed-methods study explores the self-regulation of two Japanese university students in response to the stressful situation of feeling unable to communicate effectively in English with foreigners. Qualitative data from interviews are used to interpret the quantitative results of the two students, who were part of an online intercultural Japan-Canada university exchange in which half of the communication was in English. Due to the reality check of using English for communication with foreigners, both students realized that their English communication skills were weak. Self-efficacy and coping strategies modeled by peers were internalized by one student who could subsequently cope with the demands of interacting in English, and who developed a challenge orientation and set a new goal as a result. The other student became demotivated and withdrew over time. Theories related to stress and coping, self-efficacy, peer modeling, internalization, self-regulation, and possible selves are incorporated to provide a multi-dimensional view of the processes involved in the self-regulation of these students. By looking at the experiences of the two students at the individual level, insight may be gained into the reasons behind student engagement in and withdrawal from L2 learning processes. In particular, the importance of peer modeling to positive changes in student actual and ideal selves is examined.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".