A Study on the Construction of Knowledge Mapping of University Civic and Political Science Courses and the Relevance of Their Teaching Contents
Bibliographic record
Abstract
Knowledge mapping technology can effectively integrate and manage knowledge, and fully show the relationship between knowledge.Based on this, knowledge mapping is applied to the construction of the resource base of the ideology and politics course to explore its association with the teaching content.After sorting out the relevant concepts and construction methods of knowledge mapping, this paper proposes the design method of course ideology based on knowledge mapping.The web crawler tool is utilized to crawl the text data of the Civics material and preprocess the data.The seven-step method and Protee, an important tool for ontology modeling, were used to complete the construction of the ontology model of the curriculum Civics and Politics domain.Finally, BERT, GGAT, CRF, and graph pooling techniques are combined to construct the general architecture of the Civics knowledge extraction model to realize the extraction of Civics knowledge.The method of Civics knowledge relation extraction in this paper performs well in the comparison experiment, and the AUC value of the method reaches 41.59%.More than 90% of the students express their liking and agreement with the teaching model based on knowledge graph, which verifies that the teaching model based on knowledge graph proposed in this paper has a positive and active effect on the learning aspect of students' Civics knowledge.
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How this classification was reachedexpand
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".