Exploring the role of e-learning, digital leadership and digital innovation behavior on schools' performance during society 5.0 era
Bibliographic record
Abstract
In this digital era, all human activities have moved towards digital. The digital era has provided significant changes in all aspects of life, one of which is the educational aspect. Digital technology has opened up new education opportunities but also presents challenges that must be faced. Almost all sectors, including education in the industry 5.0 era, have digitized, namely by utilizing sophisticated information technology. Era Society 5.0, is an era that will make it easier for human life to interact and transition to the digital era. Thus, the use of digital technology for every aspect of life, especially the education sector, is very necessary since it will reflect the level of competitiveness of a country. This research aims to analyze the relationship between e-learning and performance, digital leadership and performance, and the relationship between digital innovation and performance. This type of research uses quantitative research methods. The population in this research is all high school teachers who have used e-learning platforms and have carried out digital innovation. The sampling technique used in this research was a simple random sampling technique and the total sample of respondents from this research was 489 teachers. The type of data used in this research is primary data and the data search tool used is an online questionnaire using a Likert scale. The data analysis is to use structural equation modelling. The results show that e-learning had a positive and significant relationship with performance, digital leadership had a positive and significant relationship with performance and digital innovation had a positive and significant relationship with 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 | 0.010 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".