The role of information system quality and perceived usefulness on user satisfaction and sustainable e-learning
Bibliographic record
Abstract
This research aims to analyze the relationship between information system quality and user satisfaction, the relationship between perceived usefulness and user satisfaction and the relationship between user satisfaction and sustainable e-learning. The research method used in this research is associative research. The population in this study were all students who had undergone lectures using e-learning. In this study, the analytical model was used, namely Structural Equation Modeling (SEM) and the number of respondents was 456 high school students in Indonesia, who have undergone lectures using e-learning. The sampling technique used in this research is nonprobability sampling. The data collection method used was the questionnaire method and the instrument used to measure this research variable was a 5-point Likert scale. Data processing in this research uses SmartPLS software. The data analysis stage in this research is the outer model test including convergent validity, discriminant validity and composite reliability and inner model analysis, namely hypothesis testing. The results of the study indicate the quality of the information system has a positive effect on user satisfaction, Perceived Usefulness has a positive effect on user satisfaction and user satisfaction has a positive effect on sustainable e-learning. The implication is that every element at every level in formal educational institutions must pay serious attention to personal factors, including attention to student achievement expectations, relative excellence expected by students, student suitability with the e-learning system chosen in an institution's formal education, and the attitude of students who are willing to accept and continuously use e-learning. In this way, students will be consistent in using e-learning so that student achievement, which is the main goal of every school, can be achieved. The limitation of this research is that it does not provide prior understanding to respondents regarding the scope of the meaning of sustainable e-learning.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".