Smart Teaching Reform and Practice of Flipped Classroom in Culture Geography Course Based on Chaoxing Learning Platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it