The Quality of Teaching and Learning towards the Satisfaction among the University Students
Bibliographic record
Abstract
The aim of this study is to examine the relationship between the students’ perception of the quality of teaching and learning and their satisfaction in a great university in Indonesia, which was very important in the effort of upgrading the quality of higher education. A survey research involving the sample of 387 students was conducted. The research instruments of the study regarding the quality of teaching and learning consisted of aspects such as the courses, lecturers’ motivation, instructional design, relationship among students, relationship between students and lecturers, task assignment, lecturers’ efficiency, evaluation, obstacles and restraints, as well as the instrument for measuring students’ satisfaction. The research findings were analyzed using the descriptive statistics and inferential statistics such as T-Test, correlation, and regression. The findings showed that students’ perception of the quality of teaching and learning was at the moderate level, and students’ satisfaction was also at the moderate level. Based on the inferential statistics, it was indicated that there was a difference in the perception of the quality of teaching and learning concerning the gender, study period, and field of study. Similarly, there was also a difference in students’ satisfaction based on gender, study period, and field of study. The correlation analysis showed that students’ perception of the quality of teaching and learning had a positive relationship with their satisfaction, whereas the results of the regression test presented the existence of the 9 dimensions of positive contributions of the quality of teaching and learning in relation to students’ satisfaction. The study recommendation is that the university find out about students’ needs in the teaching and learning processes, including the physical needs or methods of generating a better relationship between the students and university as well as among students in order to enhance the quality of higher education. Thus, the main implication of this study regarding the education discipline was the discovery of the status of the quality of teaching and learning in an educational institution as the principal factor of the increment in students’ satisfaction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".