Kindfull-Digital Character Book Effectiveness: A User Satisfaction Approach
Bibliographic record
Abstract
This study aims to examine the effect of user competence on system quality and user satisfaction with the use of the kindfull-digital character book apps. The quality of the system is measured by indicators of flexibility and accessibility. Then, user satisfaction affected the acceptance of a system. Survey method with quantitative measurements were used. Respondents where users of the application include teachers’ and parents. Data were collected using a questionnaire. The analysis technique used is Structural Equation Modeling - Partial Least Square. The results showed that user competence had an effect on system quality. Then, competent users improve system quality. Furthermore, the quality of the system can increase user satisfaction. Satisfied users will always use IS. Thus, resulted monitoring will also be more accurate, complete and timely. Based on these results, were suggested to increase the user's knowledge and skills. So, the quality of the system will improve. The perceived satisfaction will increase their use. Then, application usage training is needed to improve their competence. Thus, the quality of the system can be improved through flexibility and accessibility. The app interface design should be made more user-friendly by providing simple features, compliant content accompanied by interested display.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".