Implementation of a Cloud Computing Based Learning Management System in Education Management
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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