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Record W2493165451 · doi:10.21083/ajote.v4i2.3095

The integration of Information and Communication Technology for teaching and learning at Ghanaian Colleges of Education: ICT Tutors’ Perceptions

2016· article· en· W2493165451 on OpenAlexvenueno aff
John K. E. Edumadze

Bibliographic record

VenueAfrican Journal of Teacher Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyCurriculumPerceptionPedagogyTechnology integrationMathematics educationPsychologySociologyTeaching methodPolitical science

Abstract

fetched live from OpenAlex

ICT is used more at the workplace than in the classroom mainly due to the lack of its extensive integrated into the curriculum by teachers. With the increasing use of ICT in our society, teachers must be at the forefront of it use in order to train their students in its proper use. Ghana’s Colleges of Education (COEs) are the first place where ICT education should begin since they are responsible for the training of teachers in the country. The main objective of the study is to evaluate the extent of ICT integration in Ghana's COEs. This study, based on the UNESCO’s literature on ICT integration in education and teachers’ adoption of ICT, examines the perception of ICT tutors in COEs on the goal to strengthen ICT curriculum in COEs. It also examines their perception of the capability of their students to competently teach ICT studies at the Basic levels in Ghana's education system. Results from the study show that tutors are of the view that ICT integration for teaching and learning are at the beginning stages with respect to Anderson’s ICT in Education model. They also are of the view that an elective ICT course should be introduced to train teacher-trainees who will specialise in ICT teaching at our basic level. Finally the study made recommendations to address these challenges.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.014
GPT teacher head0.332
Teacher spread0.318 · 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 designOther design
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

Citations9
Published2016
Admission routes1
Has abstractyes

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