Research on the Design of Curriculum System for Business Administration Students Based on Discrete Optimization Algorithm under Digital Talent Cultivation Mode
Bibliographic record
Abstract
The study of student behavior data is a necessary way to enhance the digitalization process of the curriculum system of business administration.This paper collects students' online platform learning behavior and other data, and carries out data cleaning and other pre-processing on them.Using the density function and entropy discretization algorithm to divide the continuous student data into intervals, and study the course learning characteristics of students in different attribute intervals.On this basis, optimize the curriculum system of business administration majors in colleges and universities, and judge its application value through comparative experiments.Obtain students' satisfaction data on the curriculum system of business administration majors in colleges and universities, and analyze the direction of continuous improvement.Through preprocessing and descriptive analysis, it can be judged that the student behavior data conforms to the characteristics of continuous data, and can be classified using the discretization algorithm.The student behavior data are discretized into 3 major categories and 11 subcategories, and the attribute characteristics of each category can represent the behavioral characteristics and curriculum needs of different students.The mean value of the overall satisfaction of the optimized business administration curriculum system is 3.567 points, and the scores of all dimensions are higher than those before optimization, and there is no gender difference in satisfaction (P>0.05).The entropy-based discretization algorithm can effectively support the optimization of business administration professional course system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".