Psychological Analysis of College Students Based on Improved Boolean Matrix Apriori Algorithm

2020 ◽  
Author(s):  
XU Junwen
Keyword(s):  
College Students ◽  
Boolean Matrix ◽  
Apriori Algorithm ◽  
Psychological Analysis

scholarly journals Sustainable Development of College and University Education by use of Data Mining Methods

2021 ◽  
Vol 16 (05) ◽  
pp. 102
Author(s):  
Liwen Wang ◽  
Soo-Jin Chung
Keyword(s):  
College Students ◽  
Decision Tree ◽  
Association Rules ◽  
Early Warning ◽  
Decision Tree Model ◽  
Apriori Algorithm ◽  
Tree Model ◽  
C Language ◽  
Education Management ◽  
Early Warning Mechanism

To improve the education efficiency of the students, the student-centered education plan is explored. First, the Apriori algorithm of association rules is used to mine the potential related patterns in the score data of college students and establish a reasonable teaching method. Second, aided by the decision tree model, the factors affecting students' academic performance are studied, and the potential relationship between different courses is studied. Finally, the Apriori algorithm of association rules combined with decision tree model is used to generate the early warning mechanism of students' achievement, and the course performance of college students is empirically analyzed. The results show that: C language has two sides of dependence on many subjects; higher mathematics → linear algebra → mathematical statistics → computer composition principle → computer network. The teaching scheme of C language → C + + → Java more conforms to the learning mechanism of college students. Through empirical analysis, the early warning mechanism of association rule Apriori algorithm and decision tree model can effectively analyze student's course and give student's achievement. It is found that the method proposed can provide theoretical basis for students, teachers, and university administrators to carry out education reform and education management decision-making, improve students' performance and education quality, and realize the "student-oriented" education concept, so it can be applied to the actual education management.

Improvement of Apriori Algorithm Based on Boolean Matrix

2010 ◽  
Vol 159 ◽  
pp. 144-148
Author(s):  
Bin Li Wang ◽  
Yan Guang Shen
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Boolean Matrix ◽  
Apriori Algorithm ◽  
System Cost ◽  
Transaction Database

This paper introduces the association rules and Apriori algorithm in data mining, considering the disadvantages of Apriori algorithm, a new improved Apriori algorithm based on Boolean matrix is proposed .It scans transaction database only one time, thus reduces the system cost and increases efficiency of data mining.

scholarly journals An Improved Apriori Algorithm for Association Mining Between Physical Fitness Indices of College Students

2021 ◽  
Vol 16 (09) ◽  
pp. 235
Author(s):  
Tao Pan
Keyword(s):  
College Students ◽  
Physical Fitness ◽  
Association Rule ◽  
Association Rule Mining ◽  
Vital Capacity ◽  
Correlation Coefficients ◽  
Association Mining ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Hidden Correlations

The physical fitness of college students can be evaluated scientifically based on the data of physical education (PE). This paper firstly relies on the Apriori algorithm to mine the hidden correlations between the physical fitness indices from the PE data on college students, and identify the indices closely associated with the physical fitness of college students. Then, the Apriori algorithm was improved to reduce the time complexity of association rule mining. Based on the improved algorithm, it was learned that the correlation coefficients of several indices surpassed the minimum support of 0.2 and minimum confidence of 0.7, reflecting their important impacts on physical fitness. Thus, physical fitness of college students is significantly influenced by speed, endurance, flexibility, and vital capacity, but not greatly affected by height and weight. The research results provide an important guide for the test and curriculum designs of PE for college students.

Dynamic early warning system of college students’ target course performance based on improved Apriori algorithm

2021 ◽  
pp. 1-17
Author(s):  
Guo Hao ◽  
Shaocui Guo ◽  
Guohong Sun ◽  
Wenbo Yang
Keyword(s):  
College Students ◽  
Early Warning ◽  
Educational Administration ◽  
Early Warning System ◽  
Warning System ◽  
Colleges And Universities ◽  
Apriori Algorithm ◽  
Course Performance ◽  
Course Achievement ◽  
Performance System

The early warning system of College Students’ target course achievement is an important part of the educational administration system in Colleges and universities. This paper proposes to use some techniques of association principle to mine a large amount of data in the performance system to a certain extent, and obtain available rules from the data. Based on the characteristics and shortcomings of Apriori algorithm, an improved Apriori is proposed. The algorithm can process and mine the data in the early warning system of College Students’ scores, and finally obtain the management principles, thus forming an effective early warning for the course learning. In order to promote the improvement of students’ academic performance and achieve the ultimate goal of cultivating excellent talents in Colleges and universities.

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