The integration of Information and Communication Technology for teaching and learning at Ghanaian Colleges of Education: ICT Tutors’ Perceptions
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".