Prediction of student attendance and its scheduling optimization in higher education based on multivariate regression analysis
Bibliographic record
Abstract
In order to improve the attendance rate of students and optimize the quality of teaching, this paper proposes a method of predicting the attendance rate of students in colleges and universities based on multivariate regression analysis.Firstly, we obtain the factors affecting students' attendance rate through sample survey and conduct correlation analysis, and then summarize and refine the three dimensions of students, teachers and schools.The above dimensions are used as independent variables to construct regression equations, and the regression equations are used to predict the attendance rate of students, so that teaching managers can optimize the management.The analysis found that the factors such as the college to which the truant students belonged, the reason for truancy, and the grade level showed diversity and complexity.Overall male students have more truancy rates than female students, and lecturers with higher titles have lower truancy rates.Regression modeling and prediction of truancy rate found that the prediction results are closer to the real results.Therefore, the method of this paper can be combined to optimize and adjust the attendance rate from the aspects of regulations, work allocation, teaching management and ideological education.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".