Investigating the Barriers of Creativity in the Effective Teaching and Learning Process (Case Study Lecturers and Students)
Bibliographic record
Abstract
The primary aim of this research is to explore the obstacles to creativity within the effective teaching and learning process. The research method employed is descriptive-analytical. In the qualitative component, interviews were conducted with several lecturers, while the quantitative aspect involved selecting 350 students based on the Krejcie and Morgan table, utilizing stratified sampling. The research instrument was a researcher-made questionnaire. A total of 40 faculty members were selected from a population of 600 through cluster sampling to evaluate their performance. The findings in the qualitative section indicated the lecturers' concern about grading and suggested that it is better to reduce it. In the quantitative analysis, student feedback indicated that the lecturers prioritized creativity, achieving a score of 4.06, which exceeds the average score of 3.5. The performance of academic faculty members is not significantly different in terms of faculty, academic rank, and age, but it is different in terms of gender and teaching experience. According to this research, creativity is essential, and the teaching method of faculty members and students' learning pays attention to the creative process.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".