Bibliographic record
Abstract
Abstract: While many studies into task-based interaction have been conducted within a cognitive-linguistic perspective, few have been conducted with the aim of investigating learners’ task motivation. Framed within a complex systems approach, the principle objectives of this classroom-based study were to provide a complexity description of task motivation and to identify how various socio-affective and task condition-related elements interact together to influence learner motivation during different types of tasks. The elements include task enjoyment, effort, success expectancy, relevance, emotional state, perceived difficulty, perceived group work dynamic, and specific aspects related to the structure and content of tasks. Participants for the study consisted of 38 Korean intermediate learners of English in a conversation course as part of a TESOL certificate program. Data were collected through questionnaires during the course, at pre- and post-task, and as well, through post-task interviews. Supporting the notion that task motivation functions as a complex system, learners’ motivation decreased as a result of different combinations of socio-affective variables acting together rather than in isolation. Task conditions related to cognitive complexity and topic, furthermore, were shown to function as important control parameters in the shaping of the motivational patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".