E-Service Quality in Higher Education and Frequency of Use of the Service
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it