The importance of effective learning technology utilization, teacher leadership, student engagement, and curriculum in the online learning environment
Bibliographic record
Abstract
Research has shown the effect of student engagement, teacher leadership, and curriculum on the effectiveness of the use of learning technologies and the online learning environment. The study included a total of 382 samples that included both teachers and students. Survey respondents are qualified teachers with at least 10 years of teaching experience, as determined through sampling. Participants responded to a study questionnaire that was used to collect data. Data were collected using Smart PLS software, which included validity and reliability assessments and hypothesis tests. The results of the study indicated that the dissemination of learning technology is directly affected by teacher leadership and student participation, which affects its effectiveness. Instructor leadership, student engagement, and successful use of learning technologies directly impact the online learning environment. The use of learning technology is influenced by teacher leadership, curriculum, and student engagement, which ultimately impacts the online learning environment. This study suggests two main results. To enhance the efficiency of learning technology deployment, the focus of public policy should be on enhancing teacher leadership and student performance. Moreover, enhancing the efficient use of learning technology is a critical policy goal to improve the quality of the online learning environment. Students and teachers with enhanced skills should collaborate to share their technological learning materials and management practices to improve students' online learning experiences. Subsequently, modifications were made to the curriculum and there was an increase in 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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".