SERVICE SATISFACTION: THE CASE OF A HIGHER LEARNING INSTITUTION IN MALAYSIA
Bibliographic record
Abstract
This research attempted to find out factors that affect students’ satisfaction in a higher learning Institution. The students were randomly selected from degree, masters and PhD programs to evaluate the level of students’ satisfaction. The primary data source was a questionnaire that was distributed to the students. The researchers collected 165 completed questionnaires out of a total of 190. Four factors were chosen as independent variables namely; gender, race, student status and CGPA. This study showed that the overall services offered by the university was moderate from students’ perspective. This means that the university has enough ability to continue its improvement. This study showed that the academic-related activities are more important than non academic-related such as the availability of financial advice and the level of decoration. The academic activities should not be limited to classroom activities only. It must cover everything that can develop and instill good values, attitude, character and strong personality. Universities world-wide are now competing both nationally and internationally. In order to attain new students and retain current students they should aim to enhance student satisfaction and reduce student dissatisfaction. This only can be achieved if all the services that related to academic life such as implicit services, explicit services and physical services must be delivered to a suitable standard. We also noticed that student status have an important influence on the perception of service quality. This is probably because student expectation increases as they have more contact with the university. Another element that also has influence on the perception of service quality is race and nationality. In relation to this, the academic or non-academic staffs that prepared services directly for the students should be able to identify and understand different levels of student expectations across years of study (from first year to final year) and races.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".