FACTORS AFFECTING STUDENTS’ ENROLMENT IN URBAN AND REGIONAL PLANNING HIGHER INSTITUTIONS OF LEARNING WITHIN ENUGU STATE, SOUTH EAST OF NIGERIA
Bibliographic record
Abstract
This study is aimed at determining the factors that are responsible for negative development in students’ enrolment as well as improving the popularity of Urban and Regional Planning among students during their admission. Urban and Regional Planning or Town/City/Physical Planning is a coordinating discipline concerned with spatial ordering of land uses in every environment. The research adopted survey method. Through this, quantitative analysis technique was utilized in the questionnaire representative samples data that generalized the population. The results of the students’ enrolment in Urban and Regional Planning are generally considered very poor. From the results in the table and bar chart, the Factor 1(F1): Geography as a compulsory subject for admission into Urban and Regional Planning affects students’ enrolment most with 88%. This is followed by Factor 5 (F5): Fear of unemployment after graduation. The third in affecting the enrolment is Factor 2 (F2): Course’s inadequate information; while the worst insignificant in affecting enrolment is Factor 6 (F6): High cost of the study which has 52%. The next insignificant is Factor 4 (F4): Candidates being Scared of Design with 61%, and barely insignificant being Factor 3 (F3) which is ignorance of the course’s graduates having 63%. It therefore recommended for remove of Geography as a compulsory subject for admitting students into the course study, providing solution to fear of unemployment or underemployment after graduation, sensitizing and educating people properly about the course.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".