Exploring Mathematics Instructional Strategies Working for Students with Emotional and/or Behavioural Disorders
Bibliographic record
Abstract
This study explored instructional strategies elementary-year mathematics teachers of students with emotional and/or behavioural disorders (EBD) perceived to be helpful in improving students’ performance in mathematics using a resiliency perspective (i.e., the ability to positively adapt despite experiencing significant adversity; Luthar, Cicchetti, & Becker, 2000). The researcher interviewed three elementary-year teachers to gain insight into their teaching experiences and the instructional strategies. A basic interpretive qualitative approach (Merriam, 2002) was used to understand the underlying meaning of the experiences of these mathematics teachers of students with EBD as they used evidence-based instructional strategies to improve students’ academic performance in mathematics and behaviour during instruction. A definitional focus on resiliency was the lens utilized for analyzing data generated through the interviews (Luthar, Cicchetti & Becker, 2000; Masten, 2001; Smith & Prior, 1995; Smokowski, 1998). Three themes emerged from participant interviews: ways of engaging students in learning; from dead time to active learning; and promoting positive student behaviour. Specifically, teachers reported an instructional strategy that met the needs of students of EBD which helped them obtain academic success in mathematics, and students were also better behaved in classrooms where instructional strategies employed were meeting their individual needs. These findings suggest an appropriate instructional strategy influences how students of EBD make meaning of mathematics, since teachers observed students were able to do higher thinking mathematics when strategies were in place in the classroom that met their individual needs. Teachers also shared that students were able to make good behavioural choices when they were experiencing academic success in the classroom. Practical implications of the findings, the limitations and strengths of the current study, and areas for future research are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".