Reconceptualising Formative Assessment Through Ubuntu: Advancing a Humanising Curriculum for Pre-Service Teachers in South African Higher Education
Bibliographic record
Abstract
The study writes a systematic literature review of research studies carried out from 2010 to 2025, concerned with the reformulation of formative assessment through Ubuntu and Ujamaa frameworks with focus on pre-service teacher education in South African higher education. Predominant assessment models tend to reflect individualistic, competitive paradigms, thus conflicting with indigenous African worldviews of interconnectedness and communal learning. Using Ubuntu, with special emphasis on human dignity, relationality, and empathy, and Ujamaa, with emphasis on collective responsibility and social justice, the review seeks to interrogate how the process of formative assessment can be reconfigured to promote a more humanizing and context-responsive curriculum. The findings show that African worldviews-based formative assessment practices stimulate deep engagement, affirm the identity of pre-service teachers, encourage reflective collaboration, and redress equity in historically marginalised educational settings. The study also claims to expose there are structural as well as epistemological problems, which include rigid curriculum structures, poorly trained educators, and policy-practice disconnects. It therefore recommends the integration of Afrocentric pedagogies and decolonial perspectives into assessment design for the formation of caring, critically conscious teachers. This review contributes to the discourse on educational transformation by advocating for formative assessment models that speak to African philosophies and thus to socially just teacher preparation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".