The Blended Instruction on Cloud via an Interactive Augmented Reality Technology Model to Enhance Digital Literacy
Bibliographic record
Abstract
The objectives of the study were 1) Synthesize the conceptual framework of blended instruction on the cloud via an interactive augmented reality technology model to enhance digital literacy, 2) Design the blended instruction on the cloud via an interactive augmented reality technology model, 3) Develop the blended instruction on the cloud via an interactive augmented reality technology model, and 4) Study the suitability assessment of the blended instruction on the cloud via an interactive augmented reality technology model. The proposed model develops digital literacy skills, one of the most important skills for learners in the 21st century that contributes to the learning society in the digital world. The samples group used in the study were nine experts in higher education. Then analyzing the data obtained from the assessment, using mathematic mean and standard deviation. Results of the assessment found the following. 1) The developed teaching and learning model consisted of four components: inputs, blended instruction on cloud processes, outcomes, and feedback. 2) The blended instruction on the cloud processes consists of 3 steps: the preparation, teaching and learning, presentation and summary of the learning results. 3) The assessment of the suitability of the developed teaching and learning model was at the highest appropriate. 4) The suitability assessment in the developed teaching and learning model was at the highest appropriate.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".