Pre-course student performance prediction with multi-instance multi-label learning

被引：0

作者：

Yuling MA ^{[1
,2
]}

Chaoran CUI ^{[3
]}

Xiushan NIE ^{[3
,1
]}

Gongping YANG ^{[1
]}

Kashif SHAHEED ^{[1
]}

Yilong YIN ^{[1
]}

机构：

[1] Software College, Shandong University

[2] School of Information Engineering, Shandong Yingcai College

[3] School of Computer Science and Technology, Shandong University of Finance and Economics

来源：

ScienceChina(InformationSciences) | 2019年 / 62卷 / 02期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

KNN; DT; Pre-course student performance prediction with multi-instance multi-label learning; SVM; Figure; BCS;

D O I：

暂无

中图分类号：

TP316-4 []; G642 [教学理论、教学法];

学科分类号：

040102 ; 081202 ; 0835 ;

摘要：

Dear editor,Studying courses is one of the most basic and important tasks for college students. For each new course, the initial period of learning is crucial for students, and seriously influences subsequent learning activities. However, given a large number of classes in universities, it has become impossible for teachers to keep track of the individual performance of each student. In these circumstances, it is desirable to predict each student’s performance on a certain course prior to its commencement.

引用

页码：200 / 205

页数：6

共 27 条

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