Differences in Teaching Self-Determination between General and Special Education Teachers in Elementary Schools
Bibliographic record
Abstract
The purpose of this study was to investigate whether there are differences in the teaching of self-determination between general and special education teachers in Taiwan. The participants were 380 teachers recruited from elementary schools nationwide in Taiwan. Among them, 128 were general education teachers, while the others were special educators providing services in either resource rooms (n = 125) or self-contained classrooms (n = 127). The Teaching Self-Determination Scale (TSDS) was used to collect data. Descriptive statistics, t tests, analyses of variance (ANOVAs) and multivariate analyses of variance (MANOVAs) were employed to analyze data. Findings showed that both general and special education teachers’ level of teaching self-determination was in the range of “sometimes to often”. Nevertheless, general education teachers’ level in teaching psychological empowerment, self-regulation, and autonomous skills was higher than that of their special education counterparts. Additionally, general educators tended to focus the most on instructing psychological empowerment abilities, while the self-contained classroom teachers paid intense attention to the teaching of autonomous skills. Resource room teachers demonstrated a relatively balanced instruction of various skills. Findings of this study enabled us to further understand elementary school teachers’ level of teaching self-determination and its characteristics as well. Suggestion and implications are provided.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".