Goal Setting Support in Alternative Math Classes: Effects on Motivation and Engagement
Bibliographic record
Abstract
Helping low-achieving students with learning disabilities and/or emotional-behavioural difficulties to develop the component skills for Self-Regulated Learning (SRL), such as setting and monitoring learning goals, is important for their success, both in and beyond school. This study examined the effects of a goal setting intervention on self-efficacy, motivational beliefs, and academic engagement in alternative Grade 10 mathematics classes for learners with special needs. The teacher modeled and scaffolded students’ writing of daily learning goals throughout a one-semester mathematics course, with the goal of increasing student engagement and self-efficacy in mathematics. Research questions focused on changes in students’ engagement, learning behaviours, and math-related motivational beliefs during the course, as their goal statements became more focused and descriptive. Although individual variability in responses to motivation and self-efficacy measures typified the data from this small sample of learners, the goal-setting intervention appeared to help most students to stay engaged in achievement-oriented classroom behaviour.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".