Teaching Effectiveness in Private Higher Education Institutions in Botswana: Analysis of Students’ Perceptions
Bibliographic record
Abstract
This quantitative study analyzed the perceptions of students on teaching effectiveness in private higher education institutions in Botswana. An exploratory and descriptive survey research design was adopted in this study. A valid and reliable questionnaire was used to collect data through a survey of 560 stratified randomly sampled students in private higher education institutions in Botswana. A One sample t-test and an Independent t-test were used for data analysis. A significant high level of teaching effectiveness was determined. Several items measuring teaching effectiveness contributed significantly negative to teaching effectiveness and therefore, it was recommended that lecturers should use strategies to improve on those areas of teaching to enhance their teaching. No difference in teaching effectiveness was determined with respect to age, gender and nationality of the students. However, there was a significant difference in the students’ perceptions on teaching effectiveness between the university and the non- university institutions and, lecturers were found to be more effective in their teaching at the universities as compared to the lecturers in the non -university institutions. Therefore, a further study exploring the factors contributing to such differences is recommended to improve the quality of teaching in the non- university type of private higher education institutions in Botswana.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".