@inproceedings{4ff6173302a240eca43b3c769cb67a09,
title = "Modeling MOOC student behavior with two-layer hidden markov models",
abstract = "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.",
keywords = "Hidden markov models, MOOC log analysis, Markov models, Student behavior modeling",
author = "Chase Geigle and Zhai, {Cheng Xiang}",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 4th Annual ACM Conference on Learning at Scale, L@S 2017 ; Conference date: 20-04-2017 Through 21-04-2017",
year = "2017",
month = apr,
day = "12",
doi = "10.1145/3051457.3053986",
language = "English (US)",
series = "L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale",
publisher = "Association for Computing Machinery",
pages = "205--208",
booktitle = "L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale",
address = "United States",
}