An Empirical Study on the Distinctive Teaching Mode and Practice of International Business Innovation Class in GDUFS
Bibliographic record
Abstract
Accommodating to the development of globalization, China witnesses a mounting demand for international business talents who are proficient in both foreign languages and business knowledge and adept at international cooperation and competition in business context. In order to meet this need, Guangdong University of Foreign Studies, taking advantage of its resources in foreign languages and outstanding professional teachers, made a breakthrough in multidisciplinary teaching reform and took initiavtive to set up the program of International Business Innovation Class in autumn, 2010. In July 2014, it has delivered its first batch of graduates. Hence, it is of great significance to conduct relevant investigations on the teaching mode and practice timely so as to obtain data for assessing its teaching effectiveness and students’ satisfaction. Based on empirical study, this paper evaluates three factors: learners’ recognition, features and problems of this program, which aims at identifying existing problems in practical teaching, improving teaching quality and students’ satisfaction and proposing feasible suggestions to address them. In a broader context, it attempts to provide a quotable paradigm for similar innovation programs or similar institutions’ internationalization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".