Multilevel weighted computational method of teaching quality assessment model for college courses and its validation
Bibliographic record
Abstract
This paper follows the principle of construction of evaluation index system to formulate the evaluation index system of teaching quality of college courses, which is mainly composed of 5 first-level indexes and 25 second-level indexes, and in addition, the real assessment data of ten students of a 985, 211 college on teachers’ teaching quality assessment is taken as the main source of data for this study. The combined algorithm of hierarchical analysis and fuzzy comprehensive evaluation is used to construct a university course teaching quality assessment model, and the model is analyzed by example verification. The comprehensive evaluation scores of the secondary indicators of the university’s course teaching quality are (2.1781, 2.879, 2.1934, 1.7756, 0.9739), and based on the principle of maximum affiliation degree, it is concluded that the students’ grade of the university’s course teaching quality is good (2.879), and the results are in line with the university’s actual course teaching, and at the same time, it is proved that the model of this paper has an excellent application effect.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".