E-Learning quality: The role of learning technology utilization effectiveness teacher leadership and curriculum during the pandemic season in Indonesia
Bibliographic record
Abstract
The impact of teacher leadership and curriculum on the effectiveness of learning technology utilization and the quality of e-learning has been proven by research. The study included 165 samples of teachers from Halang Island, Riau, Indonesia. Respondents are certified teachers with a minimum of ten years of experience, as determined by the purposive sampling method. Respondents completed a research questionnaire, which was used to collect data. Data ws proceeded with Smart PLS software, including validity, reliability, and hypothesis testing. The study's findings demonstrated that teacher leadership and curriculum directly impacted the effectiveness of learning technology utilization. The quality of e-learning was directly influenced by teacher leadership, curriculum, and the effectiveness of learning technology utilization. Finally, teacher leadership and curriculum impacted e-learning quality through the effectiveness of learning technology utilization. This study suggests two points. First, if you want to improve the effectiveness of learning technology utilization, the policy priority should be to update and then improve teacher leadership. Second, increasing the effectiveness of learning technology utilization is a policy priority if you want to improve the quality of e-learning. It was followed by curriculum updates and increased teacher leadership.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".