An Analysis of the Perceptions of Academic Rigor by Prince George’s County Secondary Mathematics Teachers and Secondary Administrators During the Covid-19 Crisis
Bibliographic record
Abstract
The purpose of this study was to analyze the perceptions of academic rigor by teachers and administrators of secondary mathematics classrooms in Maryland, during a crisis. The identified problem is, during COVID-19 pandemic, school district leadership may not have effectively communicated with employees about rigor application expectations. The problem developed when schools shifted to virtual learning. The following research questions were developed from teachers’ and administrators’ application of academic rigor during crisis time in Prince George’s County. RQ1: Do teachers and administrators have aligned ideas of academic rigor based on their responses and their provided evidence? RQ2: Is there alignment between the teachers and administrators’ perceptions of academic rigor during times of crisis, specifically, the COVID-19 pandemic? The research questions called for a basic qualitative study using coding, thematic, and comparison analysis approach to gather the stories of teachers and administrators. The participants were secondary (grades 6–12) teachers and administrators of mathematics in schools. A sample of 3 teachers, 4 administrators, with 2 years (minimum) experience were used. Each participant could compare teaching or monitoring during and before the COVID-19 pandemic. Responses were coded to find patterns regarding the application of rigor during COVID-19. The theory presumed in the study was if district leadership, administrators, and teachers have aligned perceptions of academic rigor in understanding and application, then academic growth can occur for students during crisis times. The results of the study were analyzed to find patterns of understanding between teachers and administrators in mathematics classrooms. The main finding of the study was that the teachers and administrators did not have an aligned perception of academic rigor during the distance learning period. District Superintendents can use this study to address academic rigor during crisis times and establish protocols ensuring that the staff have aligned perceptions of rigor. To address this concern, we had the staff in the district utilize one definition of academic rigor and use one rubric measuring the levels of rigor in classrooms. Once educators follow one rating system, they can calibrate their instructional lenses. For future research, this study can be completed in other subject areas, noting any similar concerns.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".