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Record W1612235545 · doi:10.21083/ajote.v3i3.2782

ICT LITERACY OF LANGUAGE TEACHERS IN SELECTED LAGOS STATE SECONDARY SCHOOLS, NIGERIA

2014· article· en· W1612235545 on OpenAlexvenueno aff
Oludare Adebanji Shorunke, Solomon Olanrewaju Makinde, Omawumi O. Makinde

Bibliographic record

VenueAfrican Journal of Teacher Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyLiteracyComputer literacyMathematics educationPedagogyPsychologyPolitical science

Abstract

fetched live from OpenAlex

This study contributes to the limited research available on Information and Communication Technology (ICT) literacy of language teachers in Nigeria. The advent of ICT brought new opportunities that require a skill set to operate better and faster, even in the education sector. The case for teachers’ ICT literacy is cogent in the information age to update them on their areas of specialization. The use of ICT requires some skills to enhance the access and retrieval of the required information without undue stress. The level of ICT skills a teacher possesses may affect the extent to which the teacher puts ICT to use. This study revealed that majority of the respondents made use of ICT resources. The study revealed that aggregately a large proportion of the teachers are ICT literate. This study made recommendations to improve ICT skills training and access to ICT resources.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.317
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2014
Admission routes1
Has abstractyes

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