E-Learning quality: The role of learning technology utilization effectiveness teacher leadership and curriculum during the pandemic season in Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it