Investigating the effects of e-learning, digital transformation, and digital innovation on school performance in the digital era
Bibliographic record
Abstract
In the digital era that continues to develop, information technology has brought significant changes to various aspects of human life. One of the clearest impacts is in the field of education. In this era, learning is no longer limited to physical classrooms and textbooks, instead, electronic learning or e-learning has become a popular alternative for accessing knowledge and learning online. This research aims to analyze the relationship between e-learning variables and school performance, the relationship between digital transformation variables and school performance and the relationship between digital innovation and school performance. The study uses a quantitative method approach and data analysis using the Partial Least Square -Structural Equation Modeling (PLS-SEM) approach. Research data was obtained by distributing an online questionnaire form via social media platforms. The questionnaire was designed to contain statement items on a Likert scale from 1 to 7. The respondents for this research were 467 school principals in Indonesia who were determined using the sample determination method, namely simple random sampling. The stages of research data analysis are validity testing, reliability testing and hypothesis testing or significance testing. The independent variables in this research are e-learning, digital transformation and digital innovation, the dependent variable in this research is school performance. The results indicate that e-learning, digital transformation, and innovation had positive and significant effects on school performance.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".