Status and use of information communication technology in Uganda secondary schools : teachers’ competencies, challenges, dispositions, and perceptions
Bibliographic record
Abstract
This study explored teachers’ competencies, dispositions, perceptions, and challenges in selected secondary schools in Mbale district of the Republic of Uganda. Two research questions were investigated: 1) What do teachers in Uganda perceive to be the necessary ICT competencies and dispositions in order to implement the high school curriculum? 2) What do teachers perceive as challenges to implementing ICT in curriculum and instruction? Within a sequential explanatory mixed methods research design, 243 teachers were surveyed and nine were interviewed and observed in classrooms. Exploratory factor analysis loaded six significant factors: (1) Computer use as competency indicator (α = .89); (2) Communication enhancement (α = .76): (3) Effective mediator of teaching and learning (α = .73); (4) Drafters and preparatory tool (α = .72); (5) Performance indicator (α = .64); and (6) Computer-centred pedagogy (α = .59). Computer use as competency indicator was the best predictor of the teachers’ perceptions. Qualitative thematic analysis yielded six major themes: (1) Competencies in ICT Use Depend on Training Received; (2) ICT Use is Enhanced by Teacher Characteristics or Identity; (3) ICT Use Depends on Availability of ICT Infrastructure; (4) ICT Use is Beneficial to Lesson Planning and Instruction; (5) Teacher Collaboration through ICTs has Implications for Performance; and (6) ICT-enhanced Pedagogy Requires Extra Effort and Time. Teachers indicated their competencies were hampered by the lack of technology training and adequate trainers. Teachers also indicated: resources in general were needed at the schools to enable them to integrate ICTs; and IT departments were sometimes hindrances to their efforts to adopt technology. Teachers also agreed that at times they did not use technology because it would take too much time. Implications for practice and policy touch on six main areas: (1) Enhancing classroom uses of technology; (2) providing technology training; (3) providing technology infrastructure and resources; (4) providing time; (5) modifying the school curriculum; and (6) adopting technology plans for schools. Findings suggest the Uganda government needs to commit significant funding to equip schools with resources. At the same time, findings indicate that availability of technology resources does not guarantee teacher change or student learning.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".