Bibliographic record
Abstract
Middle school is a time of significant change for students. Math programming that attends to both mathematics learning and social-emotional learning (SEL) supports students’ ability to manage emotions towards mathematics, builds confidence and agency thereby helping adolescents to stay engaged with math learning. This study investigated grade eight teachers’ practices and challenges in teaching social–emotional skills to support mathematical learning for all students. Data was collected by interviewing four grade eight teachers. Findings showed that these grade eight teachers believe that all students are capable of mathematical learning and create learning opportunities that develop students’ social–emotional learning skills and foster positive mathematical identities. These teachers used the top ten general SEL teaching strategies and SEL strategies integrated into the mathematics curriculum to support social–emotional learning skills and create learning environments that promote an academic community that values math learning. An array of SEL instructional strategies tailored to meet individual learning needs were embedded into the math instruction supporting adolescent engagement, management of negative emotions and promotion of agentic learners. This study expands our understanding of how SEL skills can complement math learning for middle school learners.The major findings include: (1) teachers believe that students’ negative emotions or poor math identity can be a barrier to students’ success in mathematical learning; (2) planning and delivering math programs that embed general, strategy-based approaches to support SEL skill development for adolescent learners complements math instruction and affirms a positive learning environment for mathematics; (3) teachers select, implement and tailor an array of SEL strategies in response to adolescent learner needs; (4) these teachers engaged in self-directed professional learning to support their early adoption and enactment of the revised curriculum; and (5) teachers use a range of SEL pedagogical strategies as part of the their recursive cycle to inform planning a mathematics program that is responsive to meeting student learning needs. The findings extend our understanding of how we can better support teachers as they implement a mathematics curriculum that encompasses a strand on SEL. A revised emerging model of the Ten Dimensions of Mathematics Education is presented with recommendations for the mathematics education research community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".