Towards Improved Teaching Effectiveness in Nigerian Public Universities: Instrument Design and Validation
Bibliographic record
Abstract
This research is conducted to examine what is currently evaluated with respect to teaching in Nigerian publicuniversities and to produce instruments that would be useful for examining the course and teaching effectiveness ofcourse lecturers. Telephone interview of ten (10) professors in ten public Nigerian Universities is used to elicitinformation on the current state of evaluation of teaching while a document analysis reveals the concerns ofNational Universities Commission with lecturers during programme accreditation. Finding indicates that teachingeffectiveness is grossly ignored in the lecturer appraisal process. An 18 item questionnaire and another 15 itemquestionnaire measuring teaching and course effectiveness respectively is constructed. After a test retest procedureusing four lecturers and four courses, the instruments yielded a reliability coefficient ranging from -0.568 to 0.591for lecturers and 0.548 to 0.944 for the courses. The correlation coefficient values clearly reveal that the courseevaluation and lecturers’ evaluation forms were adequate to generate information on the course and lecturereffectiveness. It is therefore recommended, among other things that the National Universities Commission (NUC) asa regulatory body should make the evaluation of teaching a mandatory policy for all universities.
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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 | 0.070 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".