E-Service Quality in Higher Education and Frequency of Use of the Service
Bibliographic record
Abstract
Universities have been at the forefront of online service provision. Regular evaluations and appraisals of its e-services provided to students are regularly improvised to keep pace with the rapid changes of learning technology and competitiveness of its services provided. There is a dread of research works investigating e-service quality supporting learning, research and communication and how it is related to student’s frequency of use from various sources of e-service provided to students. Data were collected from 210 students through questionnaire surveys through a structured random sampling method and analyzed statistically. The dimensions for frequency of use of e-service are from learning and research, administration, coordination, evaluation and contents storage sources. This research work has developed a single dimension comprising six elements to measure the quality of e-service in higher education namely in areas of learning, research and communication support. These elements are: 1) e-service is always available, 2) overall it is very convenience to use, 3) the user interface has a well organized appearance, 4) makes it easy to find what is needed, 5) the e-service has met needs and experience, and 6) e-service assures schedule flexibility. This study has also provided empirical evidence that there are relationships between the level and frequency in the use of e-service quality supporting learning, research and communication.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".