Predicting Success in Massive Open Online Courses (MOOCs) Using Cohesion Network Analysis

Scott Crossly, Mihai Dascalu, Danielle S. McNamara, Ryan Baker, Stefan Trausan-Matu

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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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.
Original languageEnglish
Title of host publicationMaking a Difference: Prioritizing Equity and Access in CSCL
Subtitle of host publication12th International Conference on Computer Supported Collaborative Learning
EditorsBrian K. Smith, Marcela Borge, Emma Mercier, Kyu Yon Lim
PublisherInternational Society of the Learning Sciences
Pages103-110
Volume1
ISBN (Print)978-0-9903550-0-7
Publication statusPublished - 2017
Externally publishedYes
EventMaking a Difference: Prioritizing Equity and Access in CSCL: 12th International Conference on Computer Supported Collaborative Learning - Philadelphia, United States
Duration: 18 Jun 201721 Jun 2017
https://cscl17.wordpress.com/

Conference

ConferenceMaking a Difference: Prioritizing Equity and Access in CSCL
Abbreviated titleCSCL 2017
CountryUnited States
CityPhiladelphia
Period18/06/1721/06/17
Internet address

Fingerprint

network analysis
group cohesion
student

Keywords

  • Cohesion Network Analysis
  • Massive Open Online Courses
  • prediction of completion rates
  • longitudinal analysis
  • ReaderBench framework

Cite this

Crossly, S., Dascalu, M., McNamara, D. S., Baker, R., & Trausan-Matu, S. (2017). Predicting Success in Massive Open Online Courses (MOOCs) Using Cohesion Network Analysis. In B. K. Smith, M. Borge, E. Mercier, & K. Y. Lim (Eds.), Making a Difference: Prioritizing Equity and Access in CSCL: 12th International Conference on Computer Supported Collaborative Learning (Vol. 1, pp. 103-110). International Society of the Learning Sciences.
Crossly, Scott ; Dascalu, Mihai ; McNamara, Danielle S. ; Baker, Ryan ; Trausan-Matu, Stefan. / Predicting Success in Massive Open Online Courses (MOOCs) Using Cohesion Network Analysis. Making a Difference: Prioritizing Equity and Access in CSCL: 12th International Conference on Computer Supported Collaborative Learning. editor / Brian K. Smith ; Marcela Borge ; Emma Mercier ; Kyu Yon Lim. Vol. 1 International Society of the Learning Sciences, 2017. pp. 103-110
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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.",
keywords = "Cohesion Network Analysis, Massive Open Online Courses, prediction of completion rates, longitudinal analysis, ReaderBench framework",
author = "Scott Crossly and Mihai Dascalu and McNamara, {Danielle S.} and Ryan Baker and Stefan Trausan-Matu",
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language = "English",
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volume = "1",
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Crossly, S, Dascalu, M, McNamara, DS, Baker, R & Trausan-Matu, S 2017, Predicting Success in Massive Open Online Courses (MOOCs) Using Cohesion Network Analysis. in BK Smith, M Borge, E Mercier & KY Lim (eds), Making a Difference: Prioritizing Equity and Access in CSCL: 12th International Conference on Computer Supported Collaborative Learning. vol. 1, International Society of the Learning Sciences, pp. 103-110, Making a Difference: Prioritizing Equity and Access in CSCL, Philadelphia, United States, 18/06/17.

Predicting Success in Massive Open Online Courses (MOOCs) Using Cohesion Network Analysis. / Crossly, Scott; Dascalu, Mihai; McNamara, Danielle S.; Baker, Ryan; Trausan-Matu, Stefan.

Making a Difference: Prioritizing Equity and Access in CSCL: 12th International Conference on Computer Supported Collaborative Learning. ed. / Brian K. Smith; Marcela Borge; Emma Mercier; Kyu Yon Lim. Vol. 1 International Society of the Learning Sciences, 2017. p. 103-110.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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T1 - Predicting Success in Massive Open Online Courses (MOOCs) Using Cohesion Network Analysis

AU - Crossly, Scott

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AU - McNamara, Danielle S.

AU - Baker, Ryan

AU - Trausan-Matu, Stefan

PY - 2017

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N2 - 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.

AB - 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.

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KW - Massive Open Online Courses

KW - prediction of completion rates

KW - longitudinal analysis

KW - ReaderBench framework

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SN - 978-0-9903550-0-7

VL - 1

SP - 103

EP - 110

BT - Making a Difference: Prioritizing Equity and Access in CSCL

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PB - International Society of the Learning Sciences

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Crossly S, Dascalu M, McNamara DS, Baker R, Trausan-Matu S. Predicting Success in Massive Open Online Courses (MOOCs) Using Cohesion Network Analysis. In Smith BK, Borge M, Mercier E, Lim KY, editors, Making a Difference: Prioritizing Equity and Access in CSCL: 12th International Conference on Computer Supported Collaborative Learning. Vol. 1. International Society of the Learning Sciences. 2017. p. 103-110