The Development of Virtual Online Classrooms in the Digital Age through the Application of the Metaverse Spatial Platform in the Teaching of Algorithms and Programming for First-Year Computer Studies Students
Bibliographic record
Abstract
This research aimed to develop Virtual Online Classrooms (VOC) in the digital age using the Metaverse Spatial application (Metaverse Spatial) in the Algorithms and Programming subject for first-year computer studies students. The objectives were: (1) to evaluate the instructional efficiency based on the E1/E2 criteria of 80/80, (2) to compare students’ academic achievement before and after learning through VOC in the digital age, and (3) to assess students’ satisfaction with learning through VOC supported by Metaverse Spatial. The sample consisted of 46 first-year Computer Studies students who enrolled in the Algorithms and Programming subject during the second semester of the 2024 academic year. The sample was selected using cluster random sampling. The research instruments included a learning management plan, a 45-item achievement test, and a 25-item student satisfaction questionnaire. Data were analyzed using mean, percentage, and standard deviation. The results revealed that: (1) the instructional efficiency (E1/E2) met the criteria at 83/86.8, indicating improved learning outcomes; and (2) students’ academic achievement after instruction (x̅ = 39.05, S.D. = 1.49) was significantly higher than before instruction (x̅ = 21, S.D. = 2). This confirms the effectiveness of virtual classrooms using the Metaverse Spatial application in enhancing student learning.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".