Information and Communication Technology and the Learning of English Phonetics in Selected Secondary Schools in Nigeria: A Case Study
Bibliographic record
Abstract
The study investigated the Information and Communication Technology and learning of English phonetics in selected senior secondary schools in Rivers State. The study employed a descriptive survey design. A sample of ten (10) senior secondary schools was selected from Ogba/Egbema/Ndoni Local Government Area of Rivers State. 15 students were randomly selected from the 10 schools which came to a total of (150) students based on the objectives of the study five research questions were formulated four hypotheses were tested at 0.05 level of significance. The data collected was analyzed using simple descriptive methods of mean and standard deviation (SD) to answer the five research questions. Chi-Square(x2) and t-test were used to test the hypothesis at 0.05 level of significance. The results of the research revealed that there are low availabilities of ICT in Ogba/Egbema/Ndoni Local Government Area of Rivers State. The study also revealed that teachers of oral English rarely possess the skills to use the ICT facilities to teach oral English in the public and private secondary schools in Ogba/Egbema/Ndoni Local Government Area of Rivers State. The urban areas are more exposed to the use of ICT than their mates in the rural area. Students in the rural area and their mates in the urban area have similar problems in the leaning of phonetics. Based on the results it was recommended that the use of ICT to teach oral English should be encouraged in senior secondary schools. The recommendations, suggestions for further studies and contribution to knowledge were made.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".