Research on the Organizational Characteristics of Good High School Students’ Mathematical Cognitive Structure Based on the Network Block-Modeling Analysis
Bibliographic record
Abstract
Through the direct detection and quantitative analysis of 44 concepts related to trigonometric functions in the mathematical cognitive structure of the 213 students in grade one in senior middle school, this paper finds that the mathematical cognitive structure of senior high school students has the following characteristics: In the cognitive structure of the mind, knowledge can be divided into different blocks according to the degree of relevance, the basis and the scale of the blocks, the degree of interaction in the block and between the blocks are not the same. The knowledge in a good cognitive structure should be organized in the form of blocks, and the blocks have a more scientific basis. The members in block have more obvious common features and each block is relatively large and covers more knowledge points. The block is closely linked, in addition, there must be a higher intensity of the link between the blocks, which can make the entire network of knowledge as a whole, and then it is conducive to the flow of information.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".