Modeling MOOC Student Behavior With Two-Layer Hidden Markov Models

被引：24

作者：

Geigle, Chase ^{[1
]}

Zhai, ChengXiang ^{[1
]}

机构：

[1] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA

来源：

PROCEEDINGS OF THE FOURTH (2017) ACM CONFERENCE ON LEARNING @ SCALE (L@S'17) | 2017年

基金：

美国国家科学基金会;

关键词：

MOOC log analysis; student behavior modeling; Markov models; hidden Markov models;

D O I：

10.1145/3051457.3053986

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Massive open online courses (MOOCs) provide educators with an abundance of data describing how students interact with the platform, but this data is highly underutilized today. This is in part due to the lack of sophisticated tools to provide interpretable and actionable summaries of huge amounts of MOOC activity present in log data. In this paper, we propose a method for automatically discovering student behavior patterns by leveraging the click log data that can be obtained from the MOOC platform itself in a completely unsupervised manner.

引用

页码：205 / 208

页数：4

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