Using Patterns-of-Participation Approach to Understand High School Mathematics Teachers’ Classroom Practice in Saudi Arabia
Bibliographic record
Abstract
During the past decade, the Saudi Arabian education system has undergone major changes. Government agenciesinvolved in education have introduced new policies, standards, programs, and curricula. The focus of this research is todescribe and understand high school mathematics teachers’ current practices in Saudi Arabia. This research includesthree cases of teachers currently teaching high school mathematics in Saudi Arabia. Using the Patterns of Participationconcept (PoP) as the main framework, I identified some of the significant practices, or figured worlds, from theteachers’ sense of their practices. Some of the figured worlds that emerged are mathematics, the textbook, reform, andresponsibility for students’ achievement. Mathematics, as it has always been, remains an influential figured world formathematics teachers. Reform and the textbook are becoming as influential because of the current changes in theeducation system in Saudi Arabia. While some participant teachers are developing a new understanding of whatmathematics is and what it means to teach it, they also indicated that they are mostly still using traditional teachingstrategies rather than reform teaching strategies
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".