KENYA PRIMARY SCHOOL TEACHERS’ PREPARATION IN ICT TEACHING: TEACHER BELIEFS, ATTITUDES, SELF-EFFICACY, COMPUTER COMPETENCE, AND AGE
Bibliographic record
Abstract
Information and Communication Technologies (ICT) has become globally recognized as an effective medium for learning. The Kenyan government made a commitment to provide computers for use in teaching in primary schools. This is expected to enable teachers to integrate ICT in their teaching beginning in primary standard one. Teachers will directly implement the ICT project at the classroom level, so are very crucial players to its effectiveness. This article discusses the preparedness of lower primary school teachers for this implementation process regarding their beliefs and attitudes, computer competence, and computer self-efficacy. The authors argue that the provision of computers and other infrastructure in schools may not automatically lead to integration of ICT in schools unless the government addresses teachers’ beliefs and attitudes, computer competence and their self-efficacy. The authors recommend revision of the primary teacher education preparation syllabus and training practice for pre-service teachers in ICT pedagogy to enhance their preparation to integrate ICT in their teaching in primary school.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".