Applying Measurement of Situational Self-Determination Theory to Use of a Self-Access Centre at a Japanese University
Bibliographic record
Abstract
One way to promote autonomy in the second language can be through the use of Self-access Centres (SACs). These are spaces for students to engage in activities such as self-study or communication with other learners, or native-speakers of the target language. However, merely having these spaces available does not guarantee that students will use the facility effectively, or even attend at all, so a degree of learner motivation linked with visiting the SAC would be necessary. Deci and Ryan’s (1985) Self-Determination Theory (SDT) has been used as the base for numerous studies in second language learning, including those in Japan. Proponents claim SDT is both universal and can be measured on different levels, which are global, situational and state. The authors sought to validate a measure of four subscales of SDT (Intrinsic Motivation, Identified Regulation, Introjected Regulation and External Regulation) written for this study at the situational level among undergraduates using an SAC at a Japanese University (n = 83). The rationale for items at this level comes from the field of psychology (Vallerand & Ratelle, 2002) and a study of second language constructs (Robson, 2016). A factor analysis confirmed four reliable factors, as hypothesized. Further, simplex correlations between the subconstructs somewhat confirms the underlying continuum posited by SDT researchers. These results may lead to a body of work that validates SDT theory in second language learning.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".