Integrating Indigenous Knowledge and Culturally based Activities in South African Mathematics Classrooms
Bibliographic record
Abstract
Culturally based activities embedded within indigenous knowledge, in general, may be used to support the teaching of mathematics in multicultural classes. The article reflects on research that has been conducted with twenty-five post-graduate students studying Mathematics Education at one university in KwaZulu-Natal, South Africa. These post-graduate students were also practicing mathematics teachers at schools. The study explored the use of indigenous knowledge and culturally based activities by post-graduate students in schools while teaching mathematical concepts. The theory of Realistic Mathematics Education framed this qualitative, interpretive study which used a questionnaire, lesson observations and semi-structured interviews to generate data. Qualitative data were analysed inductively and thematically. The findings reveal that the participants needed to understand indigenous knowledge to integrate culturally based activities in mathematics lessons. Secondly, culturally based activities established on indigenous knowledge scaffolded mathematics lessons and promoted the understanding of mathematical concepts to make learning more meaningful and relevant. Thirdly, this study provides examples of good practice to support teachers in integrating classroom activities and activities outside the classroom, ensuring that mathematical concepts learned in classrooms are not done in isolation but take into account learners’ authentic experiences in various settings. Finally, by integrating indigenous knowledge and culturally based activities in the mathematics curriculum, learners interacted and engaged more freely within the educational context. Similar studies could be conducted at universities internationally. Implications for mathematics teachers, mathematics teacher educators and mathematics curriculum developers globally are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| 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".