Learning communities in the crowd: Characteristics of content related interactions and social relationships in MOOC discussion forums

Abstract This mixed method study used social network analysis (SNA) and inductive qualitative analysis to compare social relationships and the underlying interactions they represent in discussions related and unrelated to the learning of course content in a statistics MOOC. It additionally examined the impact of how social relationships are conceptualized (via network tie definition) on resultant network structures and properties. Using a previously developed natural language classifier, 817 threads containing 3124 discussion posts from 567 forum participants were characterized as either related to the course content or not. Content, non-content, and overall interaction networks were constructed based on five different tie definitions: Direct Reply, Star, Direct Reply + Star, Limited Copresence, and Total Copresence. Results showed network properties were robust to differences in tie definition with the notable exception of Total Copresence. Comparison of content and non-content networks showed key differences at the network, community, and node (individual) levels. The two networks consisted of largely different people, and participants in the content network and communities had more repeated interactions with a larger number of peers. Analysis of the contributing threads helped to explain factors leading to some of these differences, showing the content discussions to be more diverse and complex in their communication purposes, conversation structures, and participants' interaction techniques. Within content discussions, the network of learners surrounding each of the two instructors showed distinct characteristics that appeared related to the instructor's facilitation approach. Finally, a group of learners tightly connected to each other through content discussions showed nascent learning community-like characteristics. This work contributes to the literature by (1) deepening understanding of MOOC discussion learning processes; (2) drawing connections between network structures and specific discussion practices; (3) providing evidence demonstrating the importance of separately examining content and non-content discussions; and (4) drawing attention to the empirical impact of the choice of tie definition in SNA studies of MOOC forums.

[1]  Carolyn Penstein Rosé,et al.  Question recommendation with constraints for massive open online courses , 2014, RecSys '14.

[2]  Anirban Dasgupta,et al.  Superposter behavior in MOOC forums , 2014, L@S.

[3]  Alyssa Friend Wise,et al.  Honing in on social learning networks in MOOC forums: examining critical network definition decisions , 2017, LAK.

[4]  Barry D. Davidson,et al.  Social networks, communication styles, and learning performance in a CSCL community , 2007, Comput. Educ..

[5]  Arthur C. Graesser,et al.  Modeling Learners' Social Centrality and Performance through Language and Discourse , 2015, EDM.

[6]  Dan Yngve Jacobsen,et al.  Dropping Out or Dropping In? A Connectivist Approach to Understanding Participants’ Strategies in an e-Learning MOOC Pilot , 2019, Technol. Knowl. Learn..

[7]  Zhenming Liu,et al.  Learning about Social Learning in MOOCs: From Statistical Analysis to Generative Model , 2013, IEEE Transactions on Learning Technologies.

[8]  Khe Foon Hew,et al.  Student perceptions of peer versus instructor facilitation of asynchronous online discussions: further findings from three cases , 2015 .

[9]  Hoi K. Suen,et al.  Examining the Relations among Student Motivation, Engagement, and Retention in a MOOC: A Structural Equation Modeling Approach. , 2015 .

[10]  William Gibson,et al.  Working with Qualitative Data , 2009, QMiP Bulletin.

[11]  Dirk Ifenthaler,et al.  Challenges for Education in a Connected World: Digital Learning, Data Rich Environments, and Computer-Based Assessment—Introduction to the Inaugural Special Issue of Technology, Knowledge and Learning , 2014, Technol. Knowl. Learn..

[12]  Carl Gutwin,et al.  The Data-Assisted Approach to Building Intelligent Technology-Enhanced Learning Environments , 2014 .

[13]  Guglielmo Trentin,et al.  The Quality-Interactivity Relationship in Distance Education. , 2000 .

[14]  Etienne Wenger,et al.  Communities of Practice: Learning, Meaning, and Identity , 1998 .

[15]  Andreas Paepcke,et al.  YouEDU: Addressing Confusion in MOOC Discussion Forums by Recommending Instructional Video Clips , 2015, EDM.

[16]  Credence Baker,et al.  The Impact of Instructor Immediacy and Presence for Online Student Affective Learning , Cognition , and Motivation , 2009 .

[17]  Alyssa Friend Wise,et al.  Unpacking the relationship between discussion forum participation and learning in MOOCs: content is key , 2018, LAK.

[18]  David E. Pritchard,et al.  Studying Learning in the Worldwide Classroom Research into edX's First MOOC. , 2013 .

[19]  Janet Mannheimer Zydney,et al.  Strategies for Creating a Community of Inquiry through Online Asynchronous Discussions , 2014 .

[20]  Alyssa Friend Wise,et al.  Identifying Content-Related Threads in MOOC Discussion Forums , 2015, L@S.

[21]  Kevin Oliver,et al.  A Social Network Perspective on Peer Supported Learning in MOOCs for Educators , 2014 .

[22]  Sebastián Ventura,et al.  Predicting students' final performance from participation in on-line discussion forums , 2013, Comput. Educ..

[23]  Erik Duval,et al.  Success, activity and drop-outs in MOOCs an exploratory study on the UNED COMA courses , 2014, LAK.

[24]  Rebecca Eynon,et al.  Communication patterns in massively open online courses , 2014, Internet High. Educ..

[25]  Caroline Haythornthwaite,et al.  Automated Discovery and Analysis of Social Networks from Threaded Discussions , 2008 .

[26]  Jure Leskovec,et al.  Overlapping community detection at scale: a nonnegative matrix factorization approach , 2013, WSDM.

[27]  Yan Zhang,et al.  Influence Analysis by Heterogeneous Network in MOOC Forums: What can We Discover? , 2015, EDM.

[28]  Dan Suthers,et al.  From contingencies to network-level phenomena: multilevel analysis of activity and actors in heterogeneous networked learning environments , 2015, LAK.

[29]  Alyssa Friend Wise,et al.  Mining for gold: Identifying content-related MOOC discussion threads across domains through linguistic modeling , 2017, Internet High. Educ..

[30]  Rebecca Eynon,et al.  Structural limitations of learning in a crowd: communication vulnerability and information diffusion in MOOCs , 2014, Scientific Reports.

[31]  Baruch B. Schwarz,et al.  Visions of CSCL: eight provocations for the future of the field , 2017, Int. J. Comput. Support. Collab. Learn..

[32]  Meaghan Brugha,et al.  EXAMINING THE LEARNING NETWORKS OF A MOOC , 2016 .

[33]  Mark Warschauer,et al.  Social Positioning and Performance in MOOCs , 2014, EDM.

[34]  Alyssa Friend Wise,et al.  Bringing order to chaos in MOOC discussion forums with content-related thread identification , 2016, LAK.

[35]  Douglas H. Fisher,et al.  Pass the Idea Please: The Relationship between Network Position, Direct Engagement, and Course Performance in MOOCs , 2017, L@S.

[36]  Norazah Yusof,et al.  Students' Interactions in Online Asynchronous Discussion Forum: A Social Network Analysis , 2009, 2009 International Conference on Education Technology and Computer.

[37]  A. Wise,et al.  The Effects of Teacher Social Presence on Student Satisfaction, Engagement, and Learning , 2004, ICLS.

[38]  Carolyn Penstein Rosé,et al.  Technology Support for Discussion Based Learning: From Computer Supported Collaborative Learning to the Future of Massive Open Online Courses , 2016, International Journal of Artificial Intelligence in Education.

[39]  E. Guba Criteria for assessing the trustworthiness of naturalistic inquiries , 1981 .

[40]  Donna Charlevoix,et al.  Do professors matter?: using an a/b test to evaluate the impact of instructor involvement on MOOC student outcomes , 2014, L@S.

[41]  Carolyn Penstein Rosé,et al.  Investigating How Student's Cognitive Behavior in MOOC Discussion Forum Affect Learning Gains , 2015, EDM.

[42]  V. Dennen,et al.  Instructor–Learner Interaction in Online Courses: The relative perceived importance of particular instructor actions on performance and satisfaction , 2007 .

[43]  Margaret Mazzolini,et al.  When to jump in: The role of the instructor in online discussion forums , 2007, Comput. Educ..

[44]  Min-Yen Kan,et al.  Learning Instructor Intervention from MOOC Forums: Early Results and Issues , 2015, EDM.

[45]  Alyssa Friend Wise,et al.  Humans and Machines Together: Improving Characterization of Large Scale Online Discussions through Dynamic Interrelated Post and Thread Categorization (DIPTiC) , 2017, L@S.

[46]  Dragan Gasevic,et al.  Translating network position into performance: importance of centrality in different network configurations , 2016, LAK.

[47]  John Scott,et al.  The SAGE Handbook of Social Network Analysis , 2011 .

[48]  Jim Hewitt Toward an Understanding of How Threads Die in Asynchronous Computer Conferences , 2005 .

[49]  A. P. Rovai Building and sustaining community in asynchronous learning networks , 2000, Internet High. Educ..

[50]  Daniel J. Brass,et al.  Network Analysis in the Social Sciences , 2009, Science.

[51]  Arthur Bangert,et al.  The influence of social presence and teaching presence on the quality of online critical inquiry , 2008, J. Comput. High. Educ..

[52]  Scott R. Klemmer,et al.  Community TAs scale high-touch learning, provide student-staff brokering, and build esprit de corps , 2014, L@S.

[53]  Vanessa P. Dennen,et al.  From Message Posting to Learning Dialogues: Factors affecting learner participation in asynchronous discussion , 2005 .

[54]  Keith Norbury Apprent-IT-Ships. , 2012 .

[55]  Edward T. Palazzolo,et al.  Exponential Random Graph (p*) Models as a Method for Social Network Analysis in Communication Research , 2010 .

[56]  Omprakash Gnawali,et al.  Language independent analysis and classification of discussion threads in Coursera MOOC forums , 2014, Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014).

[57]  Yan Zhang,et al.  Longitudinal engagement, performance, and social connectivity: a MOOC case study using exponential random graph models , 2016, LAK.

[58]  Martin Ebner,et al.  “How satisfied are you with your MOOC?” - A Research Study on Interaction in Huge Online Courses , 2013 .

[59]  Carl Auerbach,et al.  Qualitative Data: An Introduction to Coding and Analysis , 2003 .

[60]  Jo Lita Shackelford,et al.  Sense of Community in Graduate Online Education: Contribution of Learner to Learner Interaction , 2012 .