Issue Analysis of Competency-Based Mathematics Curriculum Design in African Countries: A Case Study of Mozambique’s Primary Mathematics Education
Bibliographic record
Abstract
The paper firstly clarified the characteristic of competencies being discussed in African countries by comparing them with competencies being discussed in developed countries. It has become clear that both countries are very similar. In other words, against the background of rapidly increasing internationalization and globalization, the competencies required to live in the society of the future are the same across borders, regardless of whether in a developed country or a developing country. Secondly, using Mozambique as a case study, how the competencies are actualized and what kind of challenges they face are discussed by analyzing primary mathematics curriculum, textbooks and in classes. An emphasis was placed on the ability to use social, cultural and technological tools used in an interactive manner in the competencies that were contained in the 2015 curriculum. However, most of the contents of the new textbook focus on “basic competencies” centered on basic knowledge and skills. Furthermore, there were many classes where teachers presented questions listed in the textbook as they are. Hence, it became apparent that the nurturing of practical competencies listed in the curriculum was largely reliant on the abilities of the teacher.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".