Investigation of the effect of the low learner pass rate in mathematics on unemployment in KwaZulu-Natal Schools.
Bibliographic record
Abstract
Unemployment is very high in South Africa. During the last quarter of 2016, unemployment was more than 25%. At the same time, the failure rate in mathematics in the Grade 12 examinations in South Africa is also very high. The aim of the study is to establish if there is any relationship between the high unemployment rate and the low rate of learner achievement in mathematics. Hence, the topic of the research is “Investigation of the effect of low learner pass rate in mathematics on unemployment in the KwaZulu-Natal schools”. In this study, the researcher employed both quantitative and qualitative analysis to uncover the facts about the problem at hand. In this research, the samples were taken at random and consisted of participants from the Amajuba District, to represent the province. The participants consisted of circuit managers, high school principals, deputy principals, subject heads of departments, teachers of mathematics and commerce and mathematics lecturers at Amajuba Technical College. A total of 156 questionnaires were distributed and 112 were completed and returned. Based on the questionnaire responses and the literature review, unemployment is caused by the lack of skills and the scarcity of mathematics qualifications among the workforce. Accordingly, many students fail mathematics and leave school early. Such students do not possess the skills that are required by the labour market. Hence, they constitute a considerable component of the unemployment rate.It was concluded that the low learner pass rate in mathematics actually contributes to the high unemployment rate in KwaZulu-Natal and in the whole country. The researcher recommends the establishment of a special school that would teach entrepreneurship and simultaneously re-teach mathematics to the out-of-school youth who obtained low symbols in mathematics at the Grade 12 level. The researcher further recommends the founding of an in-service centre for the newly-appointed mathematics teachers and for the teachers whose learner pass rate in mathematics is low.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".