Development of Digital Literacy and Digital Empathy with Micro-learning via Activities on Metaverse
Bibliographic record
Abstract
This research is related mainly to the study of the results on the development of digital literacy and digital empathy with micro-learning via activities on metaverse. The main concept of this study is based on the integration of micro-learning process with metaverse technology in order to encourage and provide learners with opportunities to create bodies of knowledge and engage in joint activities through the network system that can be accessed anywhere and anytime. The objectives of this research are (1) to synthesize the conceptual framework of the micro-learning via activities on metaverse, (2) to design the micro-learning process via activities on metaverse, and (3) to study the results of the development of digital literacy and digital empathy with the micro-learning via activities on metaverse. Thereby, this study relies on the pre-experimental research method with one-shot case study, in which the research participants are 30 undergraduate students of Pakse Teacher Training College, Lao People's Democratic Republic, who were derived by means of cluster sampling and well protected under the policy of confidentiality and anonymity. The research results show that (1) the students’ digital literacy and digital empathy, after learning with the micro-learning via activities on metaverse, are at very good level (mean = 43.20, SD = 2.35), and (2) the overall satisfaction towards the micro-learning via activities on metaverse is at high level (mean = 4.42, SD = 0.78). In reference to the above research results, it is evident that the micro-learning via activities on metaverse enables the students to quickly develop their digital literacy and digital empathy after receiving new experiences and new knowledge because the knowledge gained from the learning of this style is easy to remember and can be applied in an effective manner.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".