Sciences Teacher Education Curriculum Re-alignment: Science Education Lecturers’ Perspectives of Knowledge Integration at South African Universities
Bibliographic record
Abstract
A qualitative case study was conducted at six purposively sampled universities; out of a population of approximately23 universities. This sampling strategy was based on selecting some universities that became Universities ofTechnology during the process of merging Higher Education Institutions (HEIs) while other universities kept theiridentity; currently being referred to as Traditional Universities. In-depth interviews and analysis of curriculumdocuments were used as sources of data acquisition to address the aim and questions explored by this study explored;necessitated by the need to implement Minimum Requirements for Teaching Education Qualification (MRTEQ)policy guidelines. The sampled universities’ identities were concealed and pseudonyms were assigned to participantsfor ethical reasons. Qualitative methods were applied for data analysis. Findings revealed that for some institutions’integration of sub-disciples of science curriculum led to contestations and debates resulting from differentphilosophical perceptions held by subject specialists in the curriculum design process. Knowledge integrationcontinues to be a contested field in universities that typifies resistance to change. Some participants demonstrated apositive disposition towards knowledge integration models which they used in curriculum development. This studyconcludes that a collaborative and collegial deliberation among science education lecturers and experts in variousknowledge domains could be a way to find common ground on issues highlighted in this study. Re-thinking andre-conceptualising knowledge organisation for science academic knowledge are appropriate to the needs of schoolcurriculum and benefit science teachers with knowledge and competences for knowledge impartation, skills andvalues in the subject.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".