Research on the Path of Virtual Simulation Technology Promoting the Integration of Material Culture into Ideological and Political Courses in Universities
Bibliographic record
Abstract
This study focuses on the innovative paths of virtual simulation technology empowering the integration of material culture into ideological and political courses in colleges and universities. Addressing the practical dilemmas faced by traditional integration models, such as spatiotemporal limitations, content solidification, and insufficient emotional resonance, this article systematically demonstrates the unique value of virtual simulation technology in overcoming the aforementioned bottlenecks, owing to its three major characteristics: immersion, interactivity, and imagination. The study constructs an immersive teaching model of "virtual simulation + material culture museums," a concrete interpretation model of "virtual simulation + theoretical teaching," a combined exercise and training model of "virtual simulation + practical training," and a personalized expansion model of "virtual simulation + autonomous learning," forming a multi-level, three-dimensional practical scheme. Furthermore, from the aspects of resource collaborative construction, improvement of teachers' digital literacy, intelligent platform support, and multi-dimensional evaluation feedback, it proposes a systematic guarantee mechanism to ensure the effective implementation of the model, aiming to provide theoretical reference and practical guidance for promoting the digital transformation of ideological and political course teaching paradigms and enhancing the effectiveness of moral education.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".