A Comparison of Undergraduate Students’ Perception of Tutorials Before and During the COVID-19: A Case of the University of Kwazulu-Natal in the Discipline of Public Governance
Bibliographic record
Abstract
Enhancing students’ learning experience through support structures such as tutorial sessions is essential. Students attending the tutorial sessions within the Discipline of Public Governance have never been given the opportunity to provide feedback on the sessions they have attended. They only get a chance to evaluate their lecturers using closed and open-ended questions to capture their learning experiences about modules’ structure, content, delivery and assessments. This implied a need to explore the students’ perceptions about the tutorial sessions during the normal conditions and under severe conditions like this of COVID-19. The quantitative approach was utilised and the data was collected through the distribution of questionnaires to the undergraduate students. The participants attended tutorials within the Discipline of Public Governance during the first semester of the year 2020. The study findings indicated that tutorial sessions occupy a critical role in students' development and learning. It is the platform for the students to easily interact with other students, discuss issues, and improve their performance. The study recommends that higher education institutions invest in the tutorial structure as one of the student support systems as it produces positive results in enhancing student learning. Redefining and reviewing the tutorial support structure is always crucial to improve the tutorial sessions' quality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".