Regional study tours 4.0 development model design and cultural innovation research based on data analysis
Bibliographic record
Abstract
In recent years, study travel has become a popular way to expand teaching outside the classroom.Based on the trajectory of the development of study travel, the article conducts an in-depth study of the current development of study travel in the context of the new era, and explores the 4.0 model of regional study travel development.Introducing big data and new technologies into study travel and designing a digital platform for study travel.Construct the evaluation index system of study travel, and evaluate the study travel 4.0 mode through questionnaires.Detect the study effect of the study travel 4.0 mode by comparing the impact of the study travel 4.0 mode and the traditional study travel mode on students' disciplinary literacy.The comprehensive score of the evaluation of the study trip was 4.17, and the study trip 4.0 mode achieved excellent evaluation results.The experimental group and the control group did not show significant differences before the experiment, and significant differences were produced after the experiment.The experimental group's scores on each dimension of geographic literacy increased by 6.35, 5.56, 7.57, 5.01, 7.89, 5.75, and 38.13 points after the experiment, showing significant differences (p<0.05), while none of the control group's scores increased by more than 1.5 points, with p-values of greater than 0.05.The research and study trip 4.0 model has a significant positive effect on improving students' disciplinary literacy.At the same time, under the background of regional study tours, the cultural innovation strategy is put forward.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".