Application of DBSCAN clustering algorithm in evaluating students' learning status-Reference-Cited by-同舟云学术

Application of DBSCAN clustering algorithm in evaluating students' learning status

Published:2021-11 Issue: Volume: Page:
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Container-title:2021 17th International Conference on Computational Intelligence and Security (CIS)
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Author:

Du Hui¹,Chen Shiyin¹,Niu Haopeng¹,Li Yongbiao¹

Affiliation:

1. Northwest Normal University,Lanzhou,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9701656/9701587/09701684.pdf?arnumber=9701684

Reference21 articles.

1. Survey and Research on the Influence of Students' Personality on Vocational Interests in Higher Vocational Colleges;wang;Journal of Changchun University of Science and Technology (Higher Education Edition),2009

2. Review of Holland's Vocational Interest Theory and Its Application;shen;Vocational Education Research,2010

3. Holland's career choice theory and its practical application;wang;Quest,2009

4. Prediger's dimensional representation of Holland's RIASEC circumplex.

5. Introduction and Review of Holland's Occupational Interest Theory;bai;Advances in Psychological Science,1996

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