Research on the development path of “stem +” education based on digital visualization and virtual reality
Bibliographic record
Abstract
STEM education emphasizes the in-depth integration between the knowledge of different disciplines, which is based on real problem solving, aims to establish an organic link between education and life, and takes the cultivation of composite talents with a sense of innovation and hands-on ability as the fundamental purpose.Aiming at the current problems of STEM education, the development path of STEM+ education based on digital visual virtual reality is proposed.Then, combining the DEMATEL method and Interpretive Structural Modeling (ISM), the dynamic factors affecting the development of STEM+ education are explored.Finally, the fuzzy set qualitative comparative analysis (fs/QCA) method was used to analyze the group path of STEM+ education high-quality development.The results of the analysis of motivational factors show that the governmental promotion among the extrinsic motivational factors has a high centrality and is a deep factor that drives the development of STEM+ education.Synergistic motivational factors play the largest role among the three dimensions and are the key to ensure the development of STEM+ education.Endogenous motivational factors are the direct motivational factors for the development of STEM+ education and need to be focused on control.The analysis of the grouping paths in region C, for example, shows that there are two high-level grouping paths and three non-high-level grouping paths, multiple grouping paths with different paths, and high-level grouping and non-high-level grouping are in an asymmetric state.There are some differences in the grouping paths in the east, center and west, and the three regions' high-quality development of STEM+ education cannot be separated from the support of state factors and response factors.This paper provides a path reference for realizing high-level STEM+ education high-quality development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".