Learning Quality Evaluation of MOOC Based on Big Data Analysis

被引：1

作者：

Zhao, Zihao ^{[1
]}

Wu, Qiangqiang ^{[1
]}

Chen, Haopeng ^{[1
]}

Wan, Chengcheng ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Shanghai, Peoples R China

来源：

SMART COMPUTING AND COMMUNICATION, SMARTCOM 2016 | 2017年 / 10135卷

关键词：

MOOC; Learning quality evaluation; Big data analysis;

D O I：

10.1007/978-3-319-52015-5_28

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The popularity of Massive Open Online Courses has been rapidly growing recently. However, the completion rates of MOOC appear to be quite low. Moreover, the learning quality is quite doubtful for administrators of Universities since there is no suitable tools to evaluate it. Benefitting from the online environment, MOOC platforms can collect and store a huge amount of data related to learning processes. We use Storm as the parallel computing tool to accomplish the data analysis of MOOC. Our research focuses on three types of learning quality evaluation: relationship between students' forum participation and their academic performance, relationship between students' forum emotion and their academic performance, relationship between students' video seeking operation and their academic performance.

引用

页码：277 / 286

页数：10

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