Good and Caring Teaching Behaviours as Perceived by Business Education Students in Tertiary Institutions in the North Eastern Nigeria
Bibliographic record
Abstract
This study investigated good and caring teaching behaviours as perceived by Business Education students in TertiaryInstitutions in the North Eastern Nigeria. The latter needed good and caring teaching behaviours to reform theeducation sector that had been devastated by Boko Haram insurgency. The design of the study was survey. Theresearch questions answered were: (i) what knowledge bases do business education students perceived of theirLecturers for good and caring teaching in their institutions?; (ii) What repertoire of best practices do businesseducation students perceived their lecturers have for good and caring teaching in the institutions?; and (iii) whatattitude and skills of problem-solving and reflection do the students perceived of their lecturers for good and caringteaching in the tertiary institutions? An instrument of 20 items was distributed to 200 respondents in three tertiaryinstitutions located in the North Eastern Nigeria. Frequency and descriptive statistics were used to analyze the data.The findings of the study revealed, among others, that business education students perceived that their lecturers havecontrol over a knowledge base that guides what they do as Lecturers; that they also perceived that their lecturers havea repertoire of best teaching practices which they have been using to instruct them in the classrooms and to workwith fellow workers in the university setting. Lastly they perceived that their lecturers have disposition and skills toapproach all aspects of their work in a reflective, collegial and problem-solving manner. It was thereforerecommended that the relevant Tertiary Institutional Authorities and government agencies in the North EasternNigeria should provide adequate professional training and retraining of Business Education Lecturers in order to helpthem become good and caring lecturers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".