Exploration and Prospect of Future Science Teaching Mode in the Field of Metaverse
Bibliographic record
Abstract
In the context of the recent new crown pneumonia epidemic, long-duration online teaching has become a new trend in teaching development. With the continuous emergence of new information technologies, such as AR, MR, VR technologies, virtual reality resources, and network environments, the metaverse field has become an important environment for future teaching development. Based on the current demand towards virtual teaching and the recent development and improvement of the metaverse field, this paper explores the impact of the metaverse technology on science teaching, and accordingly puts forward the prospect of the science teaching model in the metaverse field, and discusses the impact of the metaverse technology on science teaching, including the teaching goals of strengthening scientific concepts, cultivating scientific thinking, encouraging inquiry practice, and clarifying attitude and responsibility; it involves the technical, environmental, and ethical conditions that need to be realized for science teaching to enter the metaverse field; the actual operation procedures that can be divided into three levels and and seven modules; the efficient, continuous, and diverse evaluation of the entire teaching activity, which provides new perspectives for solving the common problems in the current online science teaching and realizing the innovation of science teaching mode, enabling teachers and learners to accept the influence of the progress of science and technology itself in science teaching activities, and to better develop their own scientific literacy through independent inquiry and practice.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".