Implementation of a Cloud Computing Based Learning Management System in Education Management
Bibliographic record
Abstract
Modern education increasingly demands innovation in developing exciting and effective teaching materials. Augmented Reality Technology has attracted attention as a potential tool for increasing student interactivity and engagement in learning. With its ability to present additional information in a natural environment, Augmented Reality offers the opportunity to create immersive and engaging learning experiences. This research explores the use of Augmented Reality in developing interactive teaching materials, focusing on its effectiveness in increasing student understanding and facilitating more profound learning. The research method used in this study was a randomized control experiment in a secondary school. The randomized control experimental research method is used to evaluate the effects of an intervention or treatment on a group compared to a control group that did not receive the intervention or treatment. Data was collected through pre- and post-teaching comprehension tests and surveys of student satisfaction with the learning experience. The results of this research show that using interactive teaching materials based on Augmented Reality increases students’ understanding compared to using conventional teaching materials. Additionally, students in the experimental group reported higher satisfaction levels with their learning experience than the control group. This research concludes that using Augmented Reality to develop interactive teaching materials has great potential to increase learning effectiveness. By presenting additional information visually and interactively, Augmented Reality can improve students’ understanding and increase their involvement in learning. Therefore, integrating Augmented Reality in developing teaching materials can be a valuable step in improving the quality of education.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".