Attitude and Experience as Influencing Variables of Teachers’ Perception of Difficult Concepts in Primary Science in Ikom Educational Zone, Cross River State, Nigeria: The Need for Curriculum Review
Bibliographic record
Abstract
This study investigated primary school teachers’ attitude and teaching experience as some of the variables influencing teachers’ perception of difficult concepts in Primary Science in Ikom Educational Zone of Cross River State. Three null hypotheses were formulated on the basis of the identified independent variables of attitude and years of teaching experience. The teacher variables and perception of difficult concepts in Primary Science Inventory (TVPDCPSI) was developed and used in gathering data from 482 primary school teachers in 33(out of 330) primary schools in the study area as sample, using cluster and random sampling procedures and the ex-post facto design. Generated data were analyzed using population t-test, independent t-test and one way analysis of variance (ANOVA) at .05 alpha levels. It was revealed that teachers found some concepts/topics in the primary science curriculum difficult; and that their attitude and experience/years of service significantly influence their perception of this difficulty. It was concluded that teachers who cultivate negative attitude towards Primary Science, as well as those with less than five (5) years of teaching experience significantly find the teaching of the subject/concepts more difficulty than those who cultivate positive attitude. Recommendations were that compulsory teaching of Primary Science by all primary school teachers should be discouraged, while teachers should be motivated by in-service training for specialization in such specific school subjects, among others.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".