Interactive Augmented Reality Technology via Blended Instruction Lesson on Cloud
Bibliographic record
Abstract
The study aims to study the processes of the interactive augmented reality technology via blended instruction lesson on cloud, to design the interactive augmented reality technology via blended instruction lesson on cloud, to develop the interactive augmented reality technology via blended instruction lesson on cloud and to study the results of the development of interactive augmented reality technology via blended instruction lesson on cloud. The study’s sample was selected using purposive sampling, i.e., 5 people from various higher education institutions and 30 people from undergraduate students in the first year of social sciences division in Pakse Teacher Training College, Lao PDR. The results showed the following: 1) the results of the quality assessment of developed blended instruction lesson on cloud had an overall very high level (mean = 4.51, SD = 0.57); 2) the results of the developed blended instruction lesson on cloud summarised that the results of the post-test achieved a score higher than the pre-test, which is statistically significant at 0.01; 3) the results of the assessment of the digital literacy of students after studying had a good level (mean = 40.73, SD = 2.18); and 4) the results of the student satisfaction to study with the developed blended instruction lesson on cloud were found to have a high level of satisfaction (mean = 4.31, SD = 0.55).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".