Leadership Challenges in the Implementation of Ict in Public Secondary Schools, Kenya
Bibliographic record
Abstract
Many authors argue that school leadership determines how Information Communication Technology (ICT) isimplemented and its subsequent impact on teaching and learning. This involves Principal as a school leader tolead in implementation. A positive attitude of school leader towards implementation of ICT will encourage theschool community to be actively involved in its implementation.Kenya is in the process of implementing ICT in schools. However, there are many challenges that hindereffective ICT implementation including school leadership challenge. This paper reports that school leader’sinterest, their commitment and championing implementation of ICT programs in schools positively influencesthe whole process. The Paper recommends that all school leaders consider using ICT in their day-to-dayactivities of running their schools. ICT curriculum and managerial skills should be incorporated to training ofschool leaders in Kenya. Implementation of ICT is becoming more important to schools and the success of suchimplementation is often due to presence of effective school leadership.To a large extent, school leaders have been relying on government and development partners to equip schoolswith ICT infrastructure. This Paper recommends besides sensitizing development partners and waiting for theircontributions, school leadership should consider ICT a priority in school and allocate budgets that wouldpromote its implementation. A descriptive survey was used to collect data by administering questionnaires toselected sample of ICT/curriculum teachers, Principals and Board of Governors (BOG) chairpersons from 105public secondary schools in Meru County, Kenya.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 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".