Factors Related to Under Achievement in Science, Technology and Mathematics Education (STME) in Secondary Schools in Rivers State, Nigeria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We report a research into factors related to underachievement in science, technology and mathematics (STM) education in schools in Rivers State, Nigeria. The study investigated 240 Nigerian secondary school students, 100 parents’, 140 STM teachers and 20 government officials from Port Harcourt Metropolis. Five (5) research questions and one hypothesis guided the study. Separate questionnaires were used to collate data from the respondents. Frequencies, percentages, bar chart, mean, standard deviation, variance and analysis of variance were used to answer the research questions and to analysis the result from the hypothesis. The result revealed that most teachers teaching STM related subject are not qualified. They are either higher national diploma (HND) holders or engineers. The result also revealed that students’ have a negative attitude towards STM related subject, parents are too-busy to look into students school work at home and above all there is inadequate funding and a significant difference in the influence of the stakeholders on students’ performance in STM related subjects. Recommendations were made based on these findings.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it