Data of the interaction mindset questionnaire: An initial exploration
Bibliographic record
Abstract
The survey data derives from a newly-developed questionnaire of interaction mindset. Interaction mindset pertains to second language (L2) learners' disposition towards the task and/or an interlocutor prior to and/or during task-based interaction [1]. The theoretical model is consisted of five factors: (a) peer interaction; (b) collaboration; (c) form-orientation; (d) provision of peer feedback; and (e) reception of peer feedback. In the larger study ("Predicting L2 learners' noticing of L2 errors: Proficiency, language analytical ability, and interaction mindset" [2]), the questionnaire results were used as predictor variables of L2 learners' attention to language form. The current dataset contains responses from 102 L2 learners in university-level English classes. In addition to the descriptive statistics of the questionnaire, the current article reports on the results from structural equation modeling explaining the unique contributions of the five factors to the construct of interaction mindset. The model is visually depicted with a figure using AMOS. The model shows the questionnaire's potential in examining L2 learners' affective variables that may influence the learners' cognitive and behavioural engagement patterns. The entire dataset is included in an Excel file (.xlsx) and the original questionnaire is included as a supplementary file.
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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".