Analyzing the Influence of ICT Education on Academic Performance and Learning Interest of the Girl Child in Adamawa State, Nigeria.
Bibliographic record
Abstract
This paper analysis how Information and Communication Technology (ICT) education affects the academic achievement and learning motivation of female secondary school students in Adamawa State, Nigeria. The paper focused on three senatorial districts—Mubi, Yola South, and Mayo Belwa—and utilized a descriptive research design with data collected via a structured questionnaire from 150 female students. Results reveal that the availability and use of ICT tools have a positive impact on school attendance, understanding of subjects, skill development, and interest in learning, with more pronounced benefits reported in Yola South and Mayo Belwa than in Mubi. The findings also draw attention to uneven access to ICT resources, underscoring the need for fair distribution of infrastructure, teacher training, and student awareness initiatives to fully leverage ICT for girl child education. The study recommends expanding ICT facilities, improving teacher training, enhancing student ICT skills, and enacting supportive policies to ensure the sustainable integration of ICT in educational settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".