Quality Requirements for Continuous Use of E-learning Systems at Public vs. Private Universities in Spain
Bibliographic record
Abstract
During the later years of technological innovation, e-learning systems have demonstrated to be an effective way to improve educational quality and overcome time and place constraints. Virtual communication, instruction and evaluation have become an important part of the higher education. However, although e-learning has been implemented extensively, its operation and success might differ between organisations, due to institutional capacity and resources. With this in mind, the objective of this research is to distinguish between public and private universities, in the sense of the e-learning system quality and the perceived institutional support, as means to achieve users’ intention to continue using e-learning. Analysing the information from 270 Spanish teachers and students in e-learning systems at public and private universities, we concluded that information, service and educational quality determine e-learning continuous use at public universities, while perceived institutional support acts as a mediator between the information and educational quality and the continued use, in the case of the private universities. Valuable recommendations for higher-education institutions’ management suggest that innovative tools for interaction and organisation, cooperation of public and private universities, and investment in technology and human resources, are vital for continuity of e-learning systems.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".