Learner Autonomy and Curriculum Delivery in Higher Education: The Case of University of Uyo, Nigeria
Bibliographic record
Abstract
Nigeria has much hope on her higher education for the production of manpower needs of the nation. And manpower production is a function of the curriculum and its delivery modes which can only be as good as its teachers. It is no gain saying that the traditional methods of curriculum delivery can no longer serve our purpose. Efforts are being made the world over to focus on curriculum delivery modes that build on the recent technological developments and the enriched learning experiences they provide to foster learner autonomy. But the curriculum delivery modes of lecturers in higher education institutions, regarding learner autonomy is not known. This study therefore sought to find out whether the curriculum delivery mode of lecturers in the University of Uyo and their media utilization are such that can foster learner autonomy. Four hundred final year students from four out of the nine faculties were randomly selected and they responded to the questionnaire. Data were analyzed using frequencies, percentages, and one way ANOVA statistics. Results showed that lecturers’ delivery modes tended towards teacher-centredness and their media utilization was more of the low level type. Some of them were more of talk and chalk type. Among the recommendations made based on the findings of the study was the need for workshops and seminars to be organized for lecturers to acquaint them of the use of new technologies for effective curriculum delivery to achieve the goals of higher education in Nigeria. Also, lecturers should see themselves as facilitators of learning by adopting learner-centered strategies that would wean the students of their dependence to take charge of their learning and become effective learners that the nation desires.
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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 | 0.002 | 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 | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".