<p>Supporting students with learning disabilities to explore linear relationships using online learning objects</p>
Bibliographic record
Abstract
The study of linear relationships is foundational for mathematics teaching and learning. However, students’ abilities to connect different representations of linear relationships have proven to be challenging. In response, a computer-based instructional sequence was designed to support students’ understanding of the connections among representations. In this paper we report on the affordances of this dynamic mode of representation specifically for students with learning disabilities. We outline four results identified by teachers as they implemented the online lessons.Apoyo a estudiantes con problemas de aprendizaje para explorar relaciones lineales mediante el uso de objetos de aprendizaje en líneaEl estudio de las relaciones lineales es fundamental en la enseñanza y el aprendizaje de las matemáticas. Sin embargo, las habilidades de los estudiantes para conectar distintas representaciones de las relaciones lineales han demostrado ser un reto. Ante esto, hemos diseñado una secuencia de enseñanza basada en ordenadores para fomentar en los estudiantes la comprensión de las conexiones entre estas representaciones.Presentamos las potencialidades de este tipo de representación dinámica para estudiantes con dificultades de aprendizaje, destacando cuatro resultados identificados por maestros al implementar las lecciones en línea.Handle: http://hdl.handle.net/10481/21535
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".