Application of diverse teaching methodologies: a case study of the economic geography course at Northwest Normal University, China
Bibliographic record
Abstract
Economic geography stands as one of the most significant disciplines within the field of geography. This paper examines the current state of research based on the China National Knowledge Infrastructure (CNKI) and Web of Science databases, analyzing both the differences and commonalities in the teaching of ‘Economic Geography’ courses. Adopting a dual perspective of teaching theory and course practice, five distinctive teaching methods—lecture method, case teaching method, group discussion method, the three-track teaching method combining PBL (Problem-Based Learning), CBL (Case-Based Learning), and TBL (Team-Based Learning), and field internship method—were applied to economic geography courses. The most suitable teaching approach for specific knowledge points was determined. The findings reveal that despite variations in teaching approaches across different developmental stages and educational environments, the pedagogical philosophy and educational objectives of Economic Geography instruction share significant commonalities. To enhance teaching effectiveness, it is essential to integrate diverse teaching methods and establish a comprehensive pedagogical framework. Tailoring syllabi and instructional activities to the unique characteristics of the course content provides valuable insights for interpreting and delivering Economic Geography courses.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".