Leadership Challenges in the Implementation of Ict in Public Secondary Schools, Kenya
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it