Decolonising Teacher Education Curriculum in South African Higher Education
Bibliographic record
Abstract
Calls for the decolonisation of higher education in South Africa gained prominence after the #Rhodesmustfall, #Feesmustfall and series of 2015-2016 students’ protests in South African higher institutions. Visible in the demands of the students during these protests was the need for the decolonisation of higher education curriculum to ensure reflection of diverse realities in South Africa. This led to various conferences in different parts of the Republic. However, while some scholars are clamouring for the need for decolonisation, others consider the desire for decoloniality and glocalization. Thus, the subject of decolonisation remains a debate in South African society. Meanwhile, decolonisation is still very much crucial. Seemingly, in the words of Steve Biko, decolonization should begin from the mind. Hence, this discursive study explores how pre-service teachers’ minds can be decolonised for realities in transforming South African higher education. The study adopts Critical Race Theory as a lens for this phenomenon. South African higher education curriculum has predominantly been Eurocentric and epistemic, reflecting Western dominance in post-apartheid South Africa. The study argues why and how South African higher education institutions can place teacher education at the centre of learning experiences, for students to adapt and maximize the realities in their contexts, and for responsive lived experiences. Thus, adding voices to a curriculum that promotes total rethink, reflections and reconstruction of students' minds in integrating the existing Eurocentrism and epistemic knowledge with African philosophy in higher education institutions.
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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 | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".