Analysis of Undergraduates’ Compulsory Courses in China’s Comprehensive Universities – A Case Study
Bibliographic record
Abstract
Drawing on case study evidence, this article explores the development of compulsory courses in a China’s high-level comprehensive university, which has achieved good results in the procedure of Quality Assessment of Undergraduate Education (QAUE) and China Discipline Ranking (CDR) issued by Ministry of Education (MOE). The general undergraduate majors of this university are classified into 5 categories, namely, journalism and communication, economics and management, science and engineering, foreign language and literature, humanities and social science. The research scope is from grade 2007 to 2017, 2007 fall to 2018 spring semester, respectively. According to the requirements of MOE, the compulsory courses are divided into two parts: public and professional. The public part mainly refers to the courses of physical education, ideological and political and elementary computer science, while the professional part is mostly relevant to the courses associate with the major. The laws of two parts are studied by utilizing the features of course name, course ID, credits and appropriate semester. The conditions of characteristic development, the workload of teachers and students and the interdisciplinary platform, which are universal in Chinese Higher Education Institutions (HEIs), are mentioned.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".