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Record W3113076920 · doi:10.5539/hes.v11n1p42

Analysis of Undergraduates’ Compulsory Courses in China’s Comprehensive Universities – A Case Study

2020· article· en· W3113076920 on OpenAlexvenueno aff
Sai Ma, Yanrong Li, Peipei Zhang

Bibliographic record

VenueHigher Education Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationChinaWorkloadScope (computer science)IdeologySociologyJournalismQuality (philosophy)Mathematics educationPedagogyPoliticsPolitical scienceMedical educationPsychologyManagementMedia studiesLawMedicineComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.080
GPT teacher head0.401
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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