Teaching Unheard Voices: Students At-Risk in Mathematics
Bibliographic record
Abstract
To examine the mathematics classroom experiences of students considered to be “at-risk,” we conducted case studies of four students in grades 7, 8, and 9 in North- western Ontario. The case studies revealed that their mathematics classes offered few opportunities for active, engaging learning or activities that students experienced as being relevant. Their teachers, who generally used traditional practices, seemed re- luctant or unable to adopt reform-based teaching methodologies. Based on the diver- sity of the observed students’ characteristics and learning needs, and the challenges these impose on classroom dynamics, we suggest earlier intervention for at-risk stu- dents as well as more substantial professional development for teachers. Key words: mathematics learning, mathematics education, classroom environments Afin d’analyser durant les cours de mathématiques les expériences d’élèves jugés à risque, les auteurs ont effectué des études de cas portant sur quatre élèves en 7e, 8e et 9e années dans le nord-ouest de l’Ontario. Ces études de cas ont révélé que leurs cours de mathématiques leur offraient peu d’occasions d’apprentissage actif ou d’activités pertinentes que les élèves pouvaient trouver intéressantes. Leurs enseig- nants, qui en règle générale utilisaient des méthodes pédagogiques traditionnelles, semblaient peu enclins ou incapables d’adopter des méthodes dérivant de la réforme. Compte tenu de la diversité des caractéristiques et des besoins des élèves observés quant à l’apprentissage et des défis que ceux-ci imposent à la dynamique de la classe, les auteurs suggèrent une intervention plus précoce auprès des élèves à risque ainsi que des activités de perfectionnement professionnel plus systématiques pour les enseignants. Mots clés : apprentissage des mathématiques, cours de mathématiques, prestation en classe
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".