An Inquiry-Based Learning Platform Mixed with Game-Based Learning using Metaverse to Enhance Digital Literacy and Empathy Skills
Bibliographic record
Abstract
The inquiry-based learning platform mix game-based learning using metaverse, or IBL platform mixed with GBL using metaverse, was developed with an intention to enhance digital literacy and empathy skills, which are regarded as essential skills in the 21st century. The IBL platform mixed with GBL using metaverse was designed with the combination of inquiry-based learning and game-based learning processes mixed with metaverse technology. The platform developed in this study is intended to create virtual learning experiences in which learners can use their avatars to interact with the environments and other learners in metaverse. The main objective of this research were to design and study the results of the IBL platform mixed with GBL using metaverse. The population derived from purposive sampling are nine experts from different institutions with experiences in the fields of development of instruction platform and instruction systems. The results of this research show that the design of the IBL platform mixed with GBL using metaverse in terms of elements is at highest level. According to the results of this research, it can be summarized that the IBL platform mixed with GBL using metaverse contains all appropriate components and it can be employed as a guideline for learning that focuses on problem-solving processes. It is believed that the learning of this style can encourage learners to practically perform analytical thinking process in a systematic manner, and meanwhile allow them to see through the problems with systematic thinking and with the aid of technology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".