TY - GEN
T1 - Design and implementation of students' score correlation analysis system
AU - Gu, Jianhua
AU - Zhou, Xingshe
AU - Yan, Xutao
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/3/9
Y1 - 2018/3/9
N2 - To make full use of students' score and discover relationships among courses, we designed and implemented a system based on web for students' score correlation analysis. The system can find the relationships among courses with students' score or grade rank of students' score. We use the Manhattan distance and correlation coefficient to measure the correlation between two courses. The system adopts 3-tier Browser/Server architecture, which composes of a presentation layer, a domain logic layer and a data access layer. The system can draw scatter plot, calculate Manhattan distance, calculate correlation coefficient and mine association rules with students' score or grade rank of students' score. There are two kinds of correlation coefficient in the system: the Pearson and the Spearman. In order to obtain association rules meeting user requirements, the minimal support and the minimal confidence in association rules mining can be set conveniently. The analysis results are displayed in form of tables and graphics. Graphics are drawn in the Canvas element of HTML5 with JavaScript. With the help of the system, we can find relationships and association rules among different courses.
AB - To make full use of students' score and discover relationships among courses, we designed and implemented a system based on web for students' score correlation analysis. The system can find the relationships among courses with students' score or grade rank of students' score. We use the Manhattan distance and correlation coefficient to measure the correlation between two courses. The system adopts 3-tier Browser/Server architecture, which composes of a presentation layer, a domain logic layer and a data access layer. The system can draw scatter plot, calculate Manhattan distance, calculate correlation coefficient and mine association rules with students' score or grade rank of students' score. There are two kinds of correlation coefficient in the system: the Pearson and the Spearman. In order to obtain association rules meeting user requirements, the minimal support and the minimal confidence in association rules mining can be set conveniently. The analysis results are displayed in form of tables and graphics. Graphics are drawn in the Canvas element of HTML5 with JavaScript. With the help of the system, we can find relationships and association rules among different courses.
KW - Association rules
KW - Correlation analysis
KW - Correlation coefficient
KW - Manhattan distance
KW - Scatter plot
KW - Students' score
UR - http://www.scopus.com/inward/record.url?scp=85048361268&partnerID=8YFLogxK
U2 - 10.1145/3206157.3206165
DO - 10.1145/3206157.3206165
M3 - 会议稿件
AN - SCOPUS:85048361268
T3 - ACM International Conference Proceeding Series
SP - 90
EP - 94
BT - 2018 International Conference on Big Data and Education, ICBDE 2018
PB - Association for Computing Machinery
T2 - 2018 International Conference on Big Data and Education, ICBDE 2018
Y2 - 9 March 2018 through 11 March 2018
ER -