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<p>Data mining as a data analysis technology is becoming more and more widely used in the field of education. The article firstly studies the theory of data mining technology under the analysis of AI productivity, proposes an improved algorithm of Apriori to improve the mining speed of association rules, and finally proposes a model of student cultivation in higher vocational colleges and universities based on relevant theoretical research. Under the guidance of the model and assumptions, a higher vocational college in Zhejiang Province is used as the research object to verify the effectiveness of the algorithm and model proposed in this paper and to analyze the relationship between student cultivation, student performance, and student employment. By using association rules and other methods to mine student employment data and calculate the factors affecting student employment, such as the province of the employment unit, the type of city of the employment unit, the student’s major, and the student’s place of origin. From this, it can be concluded that data mining technology can provide many effective data analyses for the cultivation of students in higher vocational colleges and universities through fair and objective statistics and analysis in order to better promote the talent cultivation program of higher vocational colleges and universities.</p>
</abstract>

Data mining as a data analysis technology is becoming more and more widely used in the field of education. The article firstly studies the theory of data mining technology under the analysis of AI productivity, proposes an improved algorithm of Apriori to improve the mining speed of association rules, and finally proposes a model of student cultivation in higher vocational colleges and universities based on relevant theoretical research. Under the guidance of the model and assumptions, a higher vocational college in Zhejiang Province is used as the research object to verify the effectiveness of the algorithm and model proposed in this paper and to analyze the relationship between student cultivation, student performance, and student employment. By using association rules and other methods to mine student employment data and calculate the factors affecting student employment, such as the province of the employment unit, the type of city of the employment unit, the student’s major, and the student’s place of origin. From this, it can be concluded that data mining technology can provide many effective data analyses for the cultivation of students in higher vocational colleges and universities through fair and objective statistics and analysis in order to better promote the talent cultivation program of higher vocational colleges and universities.

The Analysis of AI Productivity and Its Implications for the Training of Students in Higher Vocational Colleges

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Data mining as a data analysis technology is becoming more and more widely used in the field of education. The article firstly studies the theory of data...

The Analysis of AI Productivity and Its Implications for the Training of Students in Higher Vocational Colleges

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 08. Apr. 2024

Akzeptiert: 05. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-1901

Schlüsselwörter
AI productivity, Association rules, Apriori algorithm, Student cultivation

© 2024 Laifang Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The Analysis of AI Productivity and Its Implications for the Training of Students in Higher Vocational Colleges

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 08. Apr. 2024

Akzeptiert: 05. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-1901

SchlüsselwörterAI productivity, Association rules, Apriori algorithm, Student cultivation

© 2024 Laifang Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
AI productivity, Association rules, Apriori algorithm, Student cultivation