Teaching and Learning in High School Online Mathematics Classrooms
Bibliographic record
Abstract
This study explores the types of instructional practices that Ontario-based mathematics teachers use in their online mathematics classrooms at the high school level to promote student engagement and achievement. The goal is to further investigate teaching practices, with a focus on understanding the types of equity-based teaching strategies that teachers implement in their online classrooms, the obstacles that they face in reaching their equity goals, the ways they build relationships with their students in an online setting, and the technology-related factors that they experience in their daily instruction. Six teachers were interviewed on this topic, and the findings were triangulated through a conceptual framework that integrates technology (teacher and student access to, and efficacy using, it), pedagogy, and the impact of the relationships that teachers build with their students, through the lens of equity. Results from the analysis indicate that teachers are currently in need of increased access to online teaching and learning resources, including professional support, especially in mathematics, which has historically been a “gatekeeper” subject. The findings carry significant implications for teacher roles and curriculum and policy development outcomes, emphasizing the need for more revision and innovation of online learning policies and mandates, especially in mathematics, as online teaching and learning continues to become more popular and prevalent in learning environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".