Revitalizing Math Education: An Instructional Approach to Addressing Socioeconomic Disparities in Grades 9 and 10 Classrooms
Bibliographic record
Abstract
The spring of 2020 has been historically marked by the emergence of the COVID-19 pandemic, bringing a substantially unique change to education by a rapid shift to remote learning. Simultaneously, several families across Ontario became impacted by financial insecurity, creating income inequalities that have continued to impact many. This qualitative study aims to explore the socioeconomic inequalities in the post-pandemic Grades 9 and 10 mathematics classrooms and their impacts on students' learner identities within mathematics education. Data was collected via two semi-structured interviews with mathematics educators from Grades 9 and 10 public secondary schools in the Greater Toronto Area. The findings suggest that educators have noticed prominent changes in the post-pandemic classroom regarding socioeconomic disparities in mathematics academic achievement and their impacts on student engagement and continued motivation within the subject, even considering the 2020 changes to the upper-intermediate curricula. Educators suggest instructional strategies to mitigate the achievement gap and create an equitable mathematics classroom to elevate student learning. Implications for education stakeholders are discussed, facilitating essential suggestions for future research to rectify the socioeconomic mathematics achievement gap in secondary classrooms.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".