@inproceedings{b0d5a12194ec4201a6bc2353e4d7c740,
title = "Predicting success in massive open online courses (Moocs) using cohesion network analysis",
abstract = "This study uses Cohesion Network Analysis (CNA) indices to identify student patterns related to course completion in a massive open online course (MOOC). This analysis examines a subsample of 320 students who completed at least one graded assignment and produced at least 50 words in discussion forums in a MOOC on educational data mining. The findings indicate that CNA indices predict with substantial accuracy (76%) whether students complete the MOOC, helping us to better understand student retention in this MOOC and to develop more actionable automated signals of student success.",
author = "Crossley, {Scott A.} and Mihai Dascalu and McNamara, {Danielle S.} and Ryan Baker and Stefan Trausan-Matu",
note = "Funding Information: This research was partially supported by the FP7 2008-212578 LTfLL project, by the 644187 EC H2020 Realising an Applied Gaming Eco-system (RAGE) project, by University Politehnica of Bucharest through the ?Excellence Research Grants? Program UPB-GEX 12/26.09.2016, as well as by the NSF grant 1417997 to Arizona State University. Publisher Copyright: {\textcopyright} ISLS.; 12th International Conference on Computer Supported Collaborative Learning - Making a Difference: Prioritizing Equity and Access in CSCL, CSCL 2017 ; Conference date: 18-06-2017 Through 22-06-2017",
year = "2017",
language = "English (US)",
series = "Computer-Supported Collaborative Learning Conference, CSCL",
publisher = "International Society of the Learning Sciences (ISLS)",
pages = "103--110",
editor = "Smith, {Brian K.} and Marcela Borge and Emma Mercier and Lim, {Kyu Yon}",
booktitle = "Making a Difference",
}