Towards an Anti-Colonial Turn in Science and Mathematics Education: Centering Indigenous Ghanaian Knowledge
Bibliographic record
Abstract
The integration of Indigenous knowledges into education is increasingly recognized as integral to creating culturally relevant and sustaining pedagogies, particularly in mathematics and science. This study explores Ghanaian primary school teachers’ knowledge and understanding of Indigenous Ghanaian knowledges (IGK) and their potential integration into classroom practice. Employing an Indigenous research methodology, data were gathered from 20 teachers through sharing circles, conversational inquiry, and a collaborative co-creation workshop. Findings reveal that teachers hold substantial knowledge of Indigenous epistemologies and recognize their relevance to mathematics and science education. However, in Ghana, significant barriers hinder the integration of these knowledges into formal education. These include a colonially rooted curriculum that marginalizes Indigenous Ghanaian knowledges, limited pedagogical training, and societal misconceptions that frame Indigenous knowledge as unscientific. Teachers articulated three distinct approaches to Indigenous knowledge integration into mathematics and science curricular: the Practical Inclusionist, which views IGK as supplementary; the Critical Incorporationist, which positions IGK as equal to Western knowledge systems; and the Integrativist, which centers IGK as foundational to teaching. Through the co-creation sessions, teachers developed exemplar lesson plans that meaningfully embed their distinct indigenous knowledges in core scientific and mathematical topics. This study highlights both the challenges and possibilities of integrating Indigenous Ghanaian knowledges into formal education and calls for a reimagining of Ghana’s mathematics and science curricula to embrace epistemic plurality and affirm Indigenous knowledges as vital to the future of science and mathematics education.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".