CHALLENGES ENCOUNTERED BY NON-SCIENCE TEACHERS IN TEACHING BASIC SCIENCE AND TECHNOLOGY IN THE NIGERIAN UNIVERSAL BASIC EDUCATION (UBE) CURRICULUM
Bibliographic record
Abstract
This study was aimed at finding out the attitude of non-science specialist teachers to teaching of basic science and technology and the difficulties they encounter while teaching the subject. The descriptive survey involved 126 Primary six non-science specialist teachers in Primary schools in Anambra State of Nigeria. The study was guided by two research questions. A structured 20-item questionnaire developed by the researchers was used to collect data. Data were analyzed using frequencies and mean. Results show that: (1) Non-science specialist teachers teaching basic science and technology have positive attitude towards the subject. (2) The teachers do not find it difficult to comprehend basic science and technology textbooks, they can operate the equipment and perform simple experiments, they can improvise the equipment and materials they use in teaching and they understand the concepts involved. (3) Inadequate teaching materials, pupils not being able to understand science and technology lessons, difficulty in explaining some concepts in mother tongue and teacher training program being mainly theory-oriented were some of the problems encountered by the non-science specialist teachers in teaching basic science and technology. Based on these results, recommendations were made some of which are that the serving teachers should be provided with regular in-service training through workshops, all pre-service (trainee) teachers should be exposed to the rudiments or introductory aspects of science and technology, and efforts should be made to create Igbo names for the science and technology terminologies and equipments to enable the teachers to communicate ideas to the pupils clearly.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".