Research on job-seeking employment and career planning of college students based on Internet big data analysis
Bibliographic record
Abstract
Under the background of big data era, big data mining technology is widely used, through data mining technology, deeper exploration of data, discovering the relevance of data, can provide decision support for decision makers.This paper analyzes the Internet big data of college students' employment decision-making based on big data mining technology, uses Apriori algorithm to mine the influencing factors of college students' vocational skills generation, meanwhile applies ID3 decision tree algorithm to analyze the college students' tendency of vocational choice, and explores the relevant factors affecting college students' employment through correlation analysis and clustering analysis.The results of the study show that students' personal, family and school have strong correlation with students' vocational skills generation, which affects the improvement of students' personal jobseeking ability.Meanwhile, the ID3 decision tree algorithm is applied to the employment consulting service for graduates to construct a career decision tree for individual college students, which visualizes their career choice paths under the influence of career values and helps them make more appropriate career choices.In addition, qualification certificates, social practice experience, academic performance, expected salary, ideal employment unit and other factors will affect the employment choice of college students, and there are individual differences among different students.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".