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Record W6968294576 · doi:10.5281/zenodo.15671918

Analyzing the Influence of ICT Education on Academic Performance and Learning Interest of the Girl Child in Adamawa State, Nigeria.

2025· article· en· W6968294576 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican Education and Politics
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsInformation and Communications TechnologyGirlLeverage (statistics)Information technologyTechnology integrationAcademic achievement

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.297
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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