The Research of Effectiveness of Ideological Political and Theories Curriculum Teaching (IPTCT) in China: Development and Problems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p class="apa">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.</p>
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it