Application of An Improved Deviation Analysis of Double Mean Data in Student’S Teaching Evaluation Data
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
This paper analyzes the main problems of College Students’ evaluation of teaching, and proposes a new method to analyze and process the evaluation data. In this paper, we first use the deviation analysis of double mean data method. Through numerical examples, we find an advantage of this method that it can effectively eliminate invalid data in the teaching evaluation data, but the result has a certain deviation from the original teaching evaluation data, and can not directly reflect the specific gap between different teachers or define the maximum and minimum of the teaching evaluation score. In order to objectively reflect the effects of teachers’ classroom teaching, we make a little improvement on the basis of this method in this paper, and give each student a certain weight, so as to get a more real and effective comprehensive evaluation score of each teacher. Numerical examples are given to compare the results of the two methods, and the improved method of deviation analysis of double mean data is more reasonable and effective.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| 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