The Research of Effectiveness of Ideological Political and Theories Curriculum Teaching (IPTCT) in China: Development and Problems
Bibliographic record
Abstract
Researchers have long been interested in how to improve the effectiveness of IPTCT courses in Chinese educational institutions. This article provides a framework for understanding the research undertaken into the effectiveness of IPTCT in China’s higher education system over the past ten years, from 2006 to 2015. It begins with a discussion of the special position held by IPTCT in China and the importance of undertaking effective studies into IPTCT. After reviewing the research on effective teaching theory within IPTCT research, describing the current research development in China, in the next section, the reasons for the use of historical and document methodology used in this paper are also examined. Next, a literature review and analysis of data collected from over a ten-year period publications in this field of study is undertaken. Then, the review highlights the six main areas identified by researchers to determine the effectiveness of IPTCT courses, including concept research; class teaching; teaching method; practice teaching; discipline innovation as well as student engagement. Furthermore, three research problems are examined which highlight the complexity of undertaking research into the effectiveness of IPTCT. The article concludes by exploring implications for future research into the effectiveness and suggestions for IPTCT teachers.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".