网络教学平台学生学习数据分析  

Analysis of Students’ Learning Data from Online Teaching Platforms

作　　者：程泽凯 佘星星 谢宁宇 CHENG Zekai;SHE Xingxing;XIE Ningyu(School of Computer Science and Technology, Anhui University of Technology, Maanshan 243032)

机构地区：[1]安徽工业大学计算机科学与技术学院

出　　处：《常州工学院学报》2019年第2期77-80,共4页Journal of Changzhou Institute of Technology

基　　金：国家自然科学基金项目(61806005)

摘　　要：网络教学平台日益普及,学生人数众多,师生之间沟通少,教师难以掌握学生学情。网络教学平台记录了详细的学生学习数据,这些数据是研究学生学习行为、帮助教师更好了解学生学习情况的重要素材。文章以网络教学平台上的“数据结构”课程为例,利用k-means聚类算法对选课学生的在线学习数据进行分析,将学生进行分类,刻画每类学生学习风格,分析学生成绩影响因素,从而改进教学方案。新教学方案使得更多学生参与到学习中,解决了学生视频观看不足、访问少、讨论少等问题,还可以为学生提供个性化教学,提高了学生学习成绩。Online teaching platforms are becoming increasingly popular and the number of students is getting larger, which leads to less communication between teachers and students.It is, therefore, difficult for teachers to grasp the learning situation of students. The online tea- ching platform retains detailed student learning data, which is important for studying students learning behavior and helping teachers better grasp students learning situation. Taking the course of “Data Structure” under the online tea- ching platform as an example, this paper uses the k-means clustering algorithm to analyze the online learning data of students who have selected the course, classifying the students, depicting the learning styles of students of each class, and analyzing the influencing factors of students scores, so as to improve the teaching program. The new teaching program enables more students to participate in learning, solves problems such as insufficient video viewing, few interviews and few discussions among students, and improves students academic performance. Meanwhile, this paper provides individualized teaching for students.

关 键 词：网络教学平台 K-MEANS聚类 学生学习风格 

分 类 号：G642[文化科学—高等教育学] TP311[文化科学—教育学]