Changes in Support Intervention Practices in Mathematics for 5-Year-Old Preschool Education: The Importance of a Collaborative and Reflective Process
Bibliographic record
Abstract
Preschool mathematics support remains insufficient, which can limit children’s skill development and impact their long-term academic success. This study explores how collaboration between researchers and teachers can enhance these practices. It is based on the Classroom Assessment Scoring System (CLASS) model, which examines three key dimensions: concept development, language modeling, and the quality of feedback. This theoretical framework highlights the importance of pedagogical interactions in supporting early mathematical learning. A mixed-methods, longitudinal approach was adopted. Over three years, six teachers participated in five collaborative sessions per year. Systematic CLASS observations, questionnaires, interviews, and reflective journals were used to assess the evolution of teaching practices. The results reveal a significant improvement in the quality of mathematics support, particularly in concept development. However, feedback and language modeling progressed more slowly. Integrating mathematics into spontaneous situations, such as free play, remains a challenge. The discussion emphasizes the importance of continuous pedagogical support to further strengthen these practices and promote more interactive and contextualized learning experiences.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".