Modelling the Influence of Teacher Characteristics on Student Achievement for Canadian Students with and without Learning Disabilities.
Bibliographic record
Abstract
The present study explored the relationships between teacher characteristics and the academic achievement of students with and without Learning Disabilities (LD) in a path model. Teacher-related variables included teacher self-efficacy, expectations of students ’ educational attainment, level of education and years of experience. Data were drawn from the Canadian National Longitudinal Survey of Children and Youth and participants included students in grades one through six who were taught by a single teacher (N = 2367). Results indicated that the hypothesized path model was an excellent fit to the data. Furthermore, academic achievement was significantly impacted by teacher expectations, LD status, and teacher efficacy. Teachers felt less confident in their ability to instruct students with LD, had lower expectations of their long-term success and also rated their achievement more poorly. The findings are discussed within existing research and implications for teacher preparation and in-service training programs are presented. Students with Learning Disabilities (LD) are now increasingly included in regular, or inclusive classrooms across North America (Data Accountability Center, 2009a). These students are typically taught by teachers who have varied training and expertise with respect to including students with exceptionalities in their classes (Booth, Nes & Stromstad, 2003). As well, these teachers bring to their classroom their own beliefs, expectations, attitudes and sense of self-efficacy related to instruction and
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".