Understanding the role of interaction in terms of linguistic, affective, and social factors: a study of native speaker - non-native speaker communication in a natural setting
Bibliographic record
Abstract
This study is intended to examine how learners' language and affective variables influence their interaction with native speakers, and conversely how such interaction influences students' language and affective variables. The affective variables are advocated to be important in the learning process. By the socio-educational model, Gardner advanced a dynamic process between affective variables and learning experiences/outcomes. Therefore, in addition to the conventional linguistic dimension that was commonly used to examine the role of interaction in second language acquisition, I added an affective dimension to the present study of interaction between Chinese ESL students and native English speakers in Canada. -- During the study, a questionnaire was used to provide quantified general descriptions of the native speaker-- non-native speaker (NS-NNS) interaction. Then, qualitative data were collected through interviews, which unveiled how individual students made sense of particular situations and experiences of interaction. Emphasis was given to the qualitative approach, and research findings were analyzed from three aspects: linguistic, affective, and social. -- In this study, the linguistic benefit of communicating with NSs was most obvious in fluency, vocabulary and expression, and sometimes pronunciation. But, except for fluency, the other two benefits were frequently situational and selective. The results indicated that, linguistically, interaction can be facilitative to language learning, but cast doubt on the proposal that interaction actually leads to acquisition. -- In terms of affective variables, as predicted by the socio-educational model, a dynamic relationship between anxiety/confidence and interaction was found in the study. L2 anxiety and confidence considerably influenced students' interaction with NSs, and conversely, interaction with NSs may gradually modify students' general anxiety and confidence level. Three factors were adopted to assess learning motivation, and they are: attitudes, desire, and intensity. I found that most students maintained very positive attitudes toward interaction with NSs, and all informants exhibited considerable desire for having more contact with NSs. However, a significant gap between students' attitude/desire and their interaction intensity was exposed. Reasons for this discrepancy were: language barriers, limited opportunities, weak social support from some NSs, and students' lack of self-discipline. It was confirmed that, through interaction with NSs, students' language was improved and personal experiences enriched, which motivated them to further communicate. – Finally, the critical perspective that looks into the social support of NS-NNS communication lent us a perspective to understand the gap between attitudes/desire and intensity found in the study. Because of subjective norms and significant others, students sometimes were unable to turn positive attitudes into practice. So this perspective is important to understanding the findings that students' anxiety, self-confidence, and motivation (esp. intensity) varied with situations and interlocutors. Such inconsistency implied that the affective filter is not an inherent trait of a language learner, but one that is socially constructed. -- So far, research findings obtained through the present study demonstrated that the effect between affective variables and interaction is reciprocal. Also it is both reasonable and desirable to interpret the role of interaction from all three aspects. \n
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".