A mobile Internet-based instructional system design incorporating DPSO discrete particle swarm algorithm to construct course arrangement application research
Bibliographic record
Abstract
This paper combines the necessary functional requirements for teaching system generated by teaching activities in the context of mobile Internet, designs the general framework of the system, users and their rights management, and constructs a set of teaching system.Subsequently, the traditional PSO algorithm is introduced, and the processing scheme of the scheduling problem is defined as particles to form an initial particle swarm, while the particle swarm position in the algorithm is updated by drawing on the crossover idea of the genetic algorithm, so as to optimize and obtain the scheduling algorithm based on DPSO.Then we test the teaching system of this paper from three levels of pressure bearing, response delay and stability performance to ensure the operating environment of the scheduling algorithm of this paper.The courses of three colleges of a university are used as experimental data to analyze the performance of the scheduling algorithm in this paper.In the comparison of course arrangement in different colleges, the adaptability of this paper's scheduling algorithm is above 0.900, while the highest adaptability of manual scheduling is only 0.8147, which indicates that compared with manual scheduling, this paper's scheduling algorithm is able to make a more reasonable course arrangement.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".