The Rational Distribution of Teaching Resources in Colleges——Course Arrangement
Bibliographic record
Abstract
Under the stimulus of national policy vigorously, the higher education obtained the rapid development. Due to the dramatic increase of students, the slow growth of teaching hardware facilities and the shortage of teacher resources, how to make use of limited resources to meet the teaching demand in the optimal form becomes a problem that needed to be solved at present. Schedule arrangement is a process full of conflicts, because there are so many limitations for teaching resources allocation such as the class time of open courses, the classes, class locations and class teachers’ factors such as. In order to improve the efficiency of running school and complete the teaching mission better, it shall use modern information technology in time and space must as far as possible to distribute the teaching resources reasonably. With the aid of optimization theory firstly, this paper establishes a preliminary scheduling optimization model based on the hard constraint conditions to make the needed number for the classroom least as far as possible. Then, we add the soft constraint conditions to the preliminary model and obtain the final optimization model. Finally, this paper adopts the way of comprehensive evaluation, constructs the index system by calculating the classroom utilization, class strength of course object, the dissatisfaction rate of soft constraint conditions, and gets the score standard of curriculum arrangement scheme. For the optimization model of this article, we are using genetic algorithm to solve the results. This paper also gives the calculation steps of genetic algorithm based on course arrangement.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".