Constraints in the use of ICT in teaching – Learning processes in secondary schools In Rongai sub county Kajiado count, Kenya
Bibliographic record
Abstract
Information communication technology (ICT) is a major drive in most world economies. It has been used in almost all the sectors of the economy. In developed countries like United States and Canada it has been incorporated in the education sectors as a tool for administration, management and in curriculum for both teaching and learning processes in most developing countries like Kenya, hence the study was geared towards secondary schools in Rongai Sub County, seeking to establish constraints in the use of ICT in teaching and learning processes in the area. It also sought to find out the level of ICT infrastructure establishment enhancing learning and teaching, to find out the extent to which teachers and students are endowed with ICT skills for used in teaching and learning process. The findings of the study will contribute information to the policy makers that could help them to formulate their teacher training programmes involving ICTs for education. The study sampled schools using purposive sampling technique using the criteria of the type of schools (boarding, day, mixed, boys or girls). Descriptive survey design was also used since it is concerned with gathering of facts. From the sampled schools an equal number of students, teachers and the principal were selected. Data was collected using questionnaires, interviews and observations. A pre-setting of research tools was carried out in one of the institutions. Data collected was analyzed descriptively using chi square and pearsons’ product moment correlation. Descriptive statistics was also used. The major findings showed that there were no adequate ICT facilities in most schools making it impossible to incorporate ICT in teaching and learning processes. Where ICT facilities were available there was no proper utilization of the facilities partly because of lack of staff. Most of the student seemed to engage in entertainment whenever they access computers mrather than using them for academic benefits. Where facilities were available there was educational programmes nor the internet. It was also found out that most teachers lacked basic computer training hence they need to address this problem. Based on this finding the study recommended that the government should assist schools to have electricity, train more staff in ICT and post them in schools, and also facilitate the provision of more computers in all the schools.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".