Beliefs and factors influencing teachers’ deployment of social media in instructional delivery in public basic schools in a developing and low-tech country
Bibliographic record
Abstract
Elaborate and detailed policies like the Accelerated Development Policy, ICT in Education Policy, and the new Standard-based Curriculum have significantly advanced technology integration in Ghanaian schools. However, integrating digital technologies like social media into public basic school curricula remains a challenge, unlike in tertiary education. This study employed a qualitative multi-case study, using semi-structured interviews and focus group discussions to investigate teachers' beliefs and factors influencing their use of social media tools for teaching. Twenty-four teachers were selected using homogeneous and convenience sampling techniques, adhering to research ethics and data quality principles. Thematic analysis revealed that teachers' beliefs about the importance, helpfulness, and competence of using social media tools significantly influenced their integration into teaching. Nevertheless, inadequate ICT infrastructure and lack of school leadership direction hindered social media deployment. To address these challenges, we recommend that the government develop and enforce technology-based teaching policies at basic schools and provide regular training programs for teachers and school administrators to enhance their capacity for effective ICT-based education implementation.
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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 | 0.003 | 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".