The influence of ecosystemic factors on Black student teachers’ perceptions and experiences of Early Childhood Education
Bibliographic record
Abstract
Post-1994 welcomed political transformation in South Africa which also brought about change in the educational sector since Black students had access to former White universities. Contrary to the growing number of Black students, the impetus for this study was found in the short supply of Black student teachers in ECE. Moreover, this study attempted to investigate how ecosystemic factors influenced the low enrolment of Black student teachers as well as their perceptions and experiences of the ECE programme at a former White university. The study was conducted from a qualitative, case study approach. The various data collection techniques, such as photo voice, narratives, semi-structured interviews and a focus group interview with five final year student teachers yielded the following results: Various ecosystemic factors influenced Black student teachers’ perceptions and experiences of ECE. First, the history of education in South Africa has had a detrimental effect on the experiences and perceptions of Black students in ECE, as well as a negative influence on the status of ECE. Second, Black students are under-prepared for their studies at tertiary level which results in negative academic experiences for the students, as well as a high dropout rate. Third, and most recurrently, it was evident that economic circumstances affect Black student teachers in terms of academic status, support offered by the HEI and enrolment into ECE teacher training. Fourth, societal factors such as the communities’ perception of, and the low regard that they hold for ECE, influenced Black students’ decisions to consider this phase of teaching as a career. Finally, personal factors, unique to each individual Black student teacher, influenced their perceptions and experiences of ECE.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".