USABILITY OF COMPUTERS IN TEACHING AND LEARNING AT TERTIARY-LEVEL INSTITUTIONS IN UGANDA
Bibliographic record
Abstract
Since a computer-enriched learning environment is positively correlated with users’ attitudes towards computers in general, the rationale of this study was to investigate the extent to which computers were applied in the teaching and learning at tertiary-level institutions; specifically at the Core Primary Teachers’ Colleges (PTCs). The study accordingly set out to examine this duo-fold ideal at Shimoni and Kibuli Core PTCs; both in Kampala District in Uganda. The specific objectives were to find out the level to which computers have been integrated in teaching and leaning at PTCs and to determine the competency of both the tutors and the students in the use of information and communication technology (ICT). Both categories served as respondents to whom a questionnaire was subjected. Findings indicated that although computers were generally being integrated in the teaching process, there was need for more guidance and support in order to ensure expertise of both tutors and students in the use of ICT. This article is cognisant that integration of technology requires a move from the traditional model of teacher presentation to a learning model whereby students draw information relevant to their future profession.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".