Research on the Design of Digital Training System for Tourism Majors and Innovation of School-Enterprise Collaboration Mode
Bibliographic record
Abstract
Teaching digitalization and integration of industry and education are developing deeply in the field of education, this study designs and constructs the digital practical training system, innovates the teaching mode of school-enterprise collaboration, and applies it to the teaching practice of tourism specialty.The performance of the digital training system for tourism majors is tested by concurrency test, business success rate test and target system thing test.Design teaching experiments to verify the teaching effect of the digital practical training system and the school-enterprise collaboration model by comparing the gaps and changes between the experimental group and the control group in the competitiveness of students' employment, the utilization rate of resources, the tourism market research, the tourism marketing, the results of the digital practical training, and the development of tourism projects.The maximum number of users in concurrent testing of the digital practical training system for tourism majors is 20, the average number is 10.182, and all the operations of users are processed, achieving good test results.Before the experiment, there is basically no difference between the two groups in the six aspects of employment competitiveness, resource utilization, tourism market research, tourism marketing, digital practical training results and tourism project development.After the experiment, the two groups showed large differences.The scores of the experimental group were higher than those of the control group in all 6 dimensions, and the difference in the scores of each dimension was more than 5 points.The teaching effectiveness of the experimental group rose more than 4.9 points in all 6 dimensions.And the score difference between the pre-and post-test of the control group is not more than 0.5 points.In this paper, digital practical training system and schoolenterprise collaboration model have better teaching effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".