Utilization of big data and artificial intelligence on quality education management and its implications on school sustainability
Bibliographic record
Abstract
Currently, school sustainability is the focus of attention of all parties, including education quality management experts, which is related to schools' weaknesses in using big data and artificial intelligence (AI). The aim of this research is to analyze the role of using big data and AI in improving the quality of education quality management in Indonesia and its impact on school sustainability. The research design uses a quantitative approach, especially correlational, verification or hypothesis testing based on empirical data in the field. The research population was all teachers, principals and high school supervisors in Palangkaraya City, Central Kalimanan, totaling around 5,423 people. The sample size used the Hair formula and obtained a sample size of 178 people. Data was collected using a questionnaire which was distributed to selected samples using a Google form. Primary data was analyzed using SMART PLS. The results shows that the use of big data had an impact on the quality education management, it also has an impact on school sustainability, while artificial intelligence had an impact on the quality education management, it also had an impact on school sustainability, In addition, quality education management had an impact on school sustainability and it mediated the relationship between the use of big data and school sustainability, and finally, quality education management mediated the relationship between artificial intelligence and school sustainability.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".