Dynamic fluctuations in foreign language enjoyment during cognitively simple and complex interactive speaking tasks
Bibliographic record
Abstract
Despite evidence on the interaction between cognitive individual differences (IDs) and task complexity, our knowledge of how affective IDs, such as foreign language enjoyment (FLE), interact with task complexity and other factors is limited. Since tasks and activities were found by Dewaele and MacIntyre (2014) to be most relevant to FLE, and since task complexity might interact with learners’ perceptions of task difficulty, it is important to investigate how task complexity impacts FLE changes. Informed by the complex dynamic systems theory, this study employed a mixed-methods multiple case study design to study patterns and causes of high and low FLE arousals. The participants were four pairs of Taiwanese high-intermediate EFL university students who were engaged in simple or complex storytelling tasks with speech acts of refusals. The speakers’ interactions were triangulated with an individual learner’s rating of FLE on a per-second scale and stimulated recalls. Results revealed idiosyncratic patterns of FLE fluctuations of peer interlocutors and a high degree of overlap in sources of low and high FLE in both groups. Speakers reported high FLE as a result of interesting storylines inherent in task design and created by peers, the use of picture prompts, peer collaboration, and task performance. Performance problems, failure to retrieve appropriate vocabulary, task design, and lack of ideas led to low FLE arousals. The findings suggest that task complexity combined with other task-induced, social, and individual factors to affect the fluctuations of FLE. Implications for task design and oral communication instruction to promote FLE are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".