COMPARATIVE EDUCATION WITH HISTORICAL SOCIOLOGY: A STUDY OF MATHEMATICS EDUCATION OF CANADA AND PAKISTAN
Bibliographic record
Abstract
The recent curricular reforms in mathematics education in Punjab, Pakistan and Ontario, Canada are studied in this work. The countries are first studied individually and then compared against each other. Using historical sociology, differences between traditional and modern education as well as the underlying theories of mathematics learning are incorporated to explain the shifts in the mathematics curricula in both the cases. We also discuss the various aspects of shifts in learning theories as mainly a tension between the traditional and progressive modes of learning. In the case of Punjab, Pakistan, it is found that the revised reform documents as well as the mathematics textbooks, do not displace the traditional views of learning mathematics as passive reception, rote memorization and reproduction of textbook questions in assessment. In the case of Ontario, Canada, shifts in Ontario mathematics curricular reforms over a decade (1995-2005) indicate adjustment and implementation towards newer learning theories based upon constructivism. This shift is claimed to be based on the 1989 NCTM reforms in the USA. This learning theory shifted considerably in the 1997 document, which is argued to be a mixture of traditionalist skill-based mathematics and the newer problem-solving based constructivist approach. The latest 2005 curriculum document, however, firmly re-focuses on problem-solving as a central feature as well as placing substantial emphasis on “mathematical processes” like communication and information technology for learning and teaching mathematics. Keywords: Mathematics Education, Comparative Education, Historical Sociology, and Curriculum Studies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.035 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".