Smart Teaching Reform and Practice of Flipped Classroom in Culture Geography Course Based on Chaoxing Learning Platform
Bibliographic record
Abstract
The smart teaching model of flipped classroom based on cloud learning platform is the trend of college classroom teaching reform. By means of the Chaoxing Learning Platform and the teaching practice of cultural geography, this paper constructs a peer instruction relied on the flipped classroom. The three stages that teachers and students need to complete, namely, before class, during class and after class can be utilized to evaluate the teaching effect of flipped classroom. Before class, students preview through micro-class resources material on Chaoxing Learning Platform provided by teachers, communicate with classmates and teachers in real time, discuss and cooperate with each other during class through cultural theme project-based learning and peer instructions, and think profoundly and self-examination after class. The analysis of students’ learning effect indicates that such a teaching mode promotes students’ subjective initiative in learning, improves the performance and comprehensive capabilities of students. As a new and efficient teaching method, “Chaoxing Learning Platform + Flipped Classroom” plays an important role in enhancing teaching quality and promoting the development of students’ comprehensive quality, such as self-directed learning, communication and cooperation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".