The role of digital leadership, system of information, and service quality on e-learning satisfaction
Bibliographic record
Abstract
This study aims to determine and analyze the influence of private university digital leadership and information system success models (system quality, information quality, service quality) on the satisfaction of e-learning users of agricultural students. This research method is a quantitative survey, and the number of research samples is 323 agricultural students who were selected by a random sampling system. The sampling technique used is simple random sampling. The analytical method used is SEM with the help of SmartPLS 3.0 software. The results show that the role of digital leadership had a significant positive effect on the three variables of the information system success model. Likewise, system quality, information quality, and service quality have a significant positive and significant effect on user satisfaction of e-learning systems. This finding can increase the exploration ability of agricultural students as users to obtain various agricultural information. This finding confirms previous studies which state that usage has a significant effect on user satisfaction. Suggestions for further research, in this study only involves a single student perspective. Future research is recommended to use the perspective of the organization/institution (e-learning system management unit), lecturers, and university employees and agricultural stakeholders.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".