Easy to use and competency development on websites as determining factors of digital learning effectivenes
Bibliographic record
Abstract
This study aims to identify the factors influencing the effectiveness of digital learning among university students. The main focus of the research is to examine the impact of easy-to-use, competency development on websites, and motivation to learn on learning effectiveness. The study employs a quantitative approach with a survey method, where data was collected through online questionnaires distributed to final-year students at Universitas PGRI Malang. Out of 300 distributed questionnaires, 204 were returned and deemed complete, thus used as the final sample for analysis. The data were analyzed using Structural Equation Modeling (SEM) techniques with the help of SmartPLS software to examine the relationships between variables. The results show that ease of use has a significant impact on motivation to learn, but does not have a direct impact on learning effectiveness. Competency development on websites does not have a significant impact on motivation to learn, but it does have a significant impact on learning effectiveness. Additionally, motivation to learn was found to have a significant impact on learning effectiveness and mediates the relationship between ease of use and learning effectiveness, but does not mediate the relationship between competency development on websites and learning effectiveness.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".