Use of Virtual Learning on Academic Performance of Js 1 Integrated Science Student in Secondary School in Port Harcourt Local Government Area
Bibliographic record
Abstract
This paper examined the use of virtual learning on the academic performance of JS 1 Integrated Science Students in Secondary Schools in Port Harcourt Local Government Area of Rivers State, Nigeria. In discussing this, it examined the concept of virtual learning instruction, Synchronous and Asynchronous e-learning as compared to face-to-face traditional classroom learning and the advantages and disadvantages of virtual learning. Quasi experimental design was adopted. Population for the study comprised all JS 1 students in public secondary schools in Port Harcourt Local Government Area. Sample size of 200 students were selected from 2,910 JS 1 students in all the public secondary schools in Port Harcourt Local Government Area using the multi-stage sampling technique. Instrument for data collection was an objective Integrated Science performance test (OIPT). Two hypotheses were formulated and tested using Factorial Analysis of Variance (ANCOVA). Result of the analysis showed that there is no significant difference in student’s academic performance when virtual learning and expository methods are used in teaching Integrated Science in Junior Secondary class (f1.195 = 1.606, p > .05) and that there is no significant difference between the academic performance of female and male students when virtual learning and expository methods are used in teaching Integrated Science in JS 1 (F1, 195 = 717, p > .05). It was therefore recommended that: Teachers should try to apply the virtual learning instruction in the teaching of Integrated Science as this was found to impact on the students achievement positively and that male and female students should be equally engaged in the learning of Integrated Science where VLS is incorp+orated to eliminate the gender bias in sciences.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".