Ghanaian Educational Institutions' Capacity for, and Approach to, ICT Pedagogical Integration
Bibliographic record
Abstract
The paper reports some of the findings of Ghana's participation in the Pan African Research Agenda on the Pedagogical Integration of ICT. The study examines Ghanaian educational institutions' capacity for, and approach to, ICT pedagogical integration. A Junior High School, three Senior High schools and a Teacher Education University are sampled by a University of Education Winneba (UEW) based research team according to given guidelines. The study combines document analysis and survey techniques with the use of structured questionnaire, class observation checklists and interview schedules to collect qualitative and quantitative data which have been uploaded onto an open online observatory at www.observatoiretic.org. The results indicate that some attempts had been made by the Ministry of Education to formalize the teaching of ICT literacy and encourage its integration into the teaching and learning process. Nonetheless, very little integration is observed in teaching and learning in schools. This is found to be due in part to inefficiencies in the design of the curriculum and partly to the inadequate ICT resources (both material and human) available. It is recommended that the Ministry of Education should review the curricula at various levels to ensure ICT pedagogical integration in their implementation and make available sufficient resources for ongoing training and support for teachers to model the new pedagogies and tools for learning. Keywords: ICT literacy, ICT pedagogical integration, Inhibiting factors.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".