The nature and level of learner–learner interaction in a chemistry massive open online course (MOOC)

Similar to other online courses, massive open online courses (MOOCs) often rely on learner–learner interaction as a mechanism to promote learning. However, little is known at present about learner–learner interaction in these nascent informal learning environments. While some studies have explored MOOC participant perceptions of learner–learner interactions, research is still lacking regarding the content and level of such interactions. Using the interaction analysis model (IAM) as a theoretical framework and social network analysis methods, the present study investigates the nature and level of learner–learner interaction within a popular Chemistry MOOC from Coursera. Findings suggest that learner–learner interaction: was limited to lower phases of the IAM framework (e.g., sharing and comparing information); changed (decreased) over time; and was heavily dependent on a few highly-engaged learners. Potential implications for the design of future MOOCs are discussed.

[1]  Alejandro Armellini,et al.  Expanding the Interaction Equivalency Theorem , 2015 .

[2]  Gayle S. Christensen,et al.  The MOOC Phenomenon: Who Takes Massive Open Online Courses and Why? , 2013 .

[3]  F. Fischer,et al.  Knowledge convergence in collaborative learning: Concepts and assessment , 2007 .

[4]  S. Tan,et al.  Professional Development of Teachers for Computer-Supported Collaborative Learning: A Knowledge-Building Approach , 2009, Teachers College Record: The Voice of Scholarship in Education.

[5]  Charles Juwah.  Interactions in online education : implications for theory and practice , 2006 .

[6]  M. Moore.  Editorial: Three types of interaction , 1989 .

[7]  Kueh Chin Yap,et al.  Knowledge construction and misconstruction: A case study approach in asynchronous discussion using Knowledge Construction - Message Map (KCMM) and Knowledge Construction - Message Graph (KCMG) , 2010, Comput. Educ..

[8]  K. Hew,et al.  Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges , 2014 .

[9]  Osmar R. Zaïane,et al.  Collaborative Learning of Students in Online Discussion Forums: A Social Network Analysis Perspective , 2014 .

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

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

[12]  Sangchul Oh,et al.  Scaffolding online argumentation during problem solving , 2007, J. Comput. Assist. Learn..

[13]  David W. Johnson,et al.  Cooperation and the Use of Technology , 2007 .

[14]  J. Daniel,et al.  Making Sense of MOOCs : Musings in a Maze of Myth , Paradox and Possibility Author : , 2013 .

[15]  Carlos Delgado Kloos,et al.  Who are the top contributors in a MOOC? Relating participants' performance and contributions , 2016, J. Comput. Assist. Learn..

[16]  Yin Yin,et al.  A phenomenology of learning large: the tutorial sphere of xMOOC video lectures , 2014 .

[17]  Kerstin E. E. Schroder,et al.  Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses , 2014, Internet High. Educ..

[18]  Todd D. Reeves,et al.  Predictors of teacher satisfaction with online professional development: evidence from the USA’s e‐Learning for Educators initiative , 2011 .

[19]  Katy Jordan,et al.  Initial trends in enrolment and completion of massive open online courses , 2014 .

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

[21]  Mike Moore,et al.  Distance Education: A Systems View , 1995 .

[22]  Hossam Haick,et al.  Motivation to learn in massive open online courses: Examining aspects of language and social engagement , 2016, Comput. Educ..

[23]  António Moreira,et al.  Assessing social construction of knowledge online: A critique of the interaction analysis model , 2014, Comput. Hum. Behav..

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

[25]  C. Hmelo‐Silver,et al.  Seven Affordances of Computer-Supported Collaborative Learning: How to Support Collaborative Learning? How Can Technologies Help? , 2016 .

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

[27]  Marion Waite,et al.  Liminal Participants and Skilled Orienteers: Learner Participation in a MOOC for New Lecturers , 2013 .

[28]  Daniel D. Suthers,et al.  Computer-supported collaborative learning: An historical perspective , 2006 .

[29]  J. Michael Spector,et al.  Remarks on MOOCS and Mini-MOOCS , 2014, Educational Technology Research and Development.

[30]  Tekeisha Zimmerman,et al.  Exploring Learner to Content Interaction as a Success Factor in Online Courses , 2012 .

[31]  Peter Goodyear,et al.  Computer-Supported Collaborative Learning: Instructional Approaches, Group Processes and Educational Designs , 2014 .

[32]  Francisco J. García-Peñalvo,et al.  A methodology proposal for developing adaptive cMOOC , 2013, TEEM '13.

[33]  Michael J. Hannafin,et al.  Scaffolding problem solving in technology-enhanced learning environments (TELEs): Bridging research and theory with practice , 2011, Comput. Educ..

[34]  Susanne P. Lajoie,et al.  Scaffolding problem-based learning with CSCL tools , 2010, Int. J. Comput. Support. Collab. Learn..

[35]  Miroslava Raspopovic,et al.  Success factors for e-learning in a developing country: A case study of Serbia , 2014 .

[36]  Gi Woong Choi,et al.  Understanding MOOC students: motivations and behaviours indicative of MOOC completion , 2016, J. Comput. Assist. Learn..

[37]  Paula de Barba,et al.  The role of students' motivation and participation in predicting performance in a MOOC , 2016, J. Comput. Assist. Learn..

[38]  Pin-Ju Chen,et al.  MOOC study group: Facilitation strategies, influential factors, and student perceived gains , 2015, Comput. Educ..

[39]  Ghada R. El Said,et al.  Exploring the factors affecting MOOC retention: A survey study , 2016, Comput. Educ..

[40]  John Fritz,et al.  Classroom walls that talk: Using online course activity data of successful students to raise self-awareness of underperforming peers , 2011, Internet High. Educ..

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

[42]  Shirley Williams,et al.  MOOCs: A systematic study of the published literature 2008-2012 , 2013 .

[43]  Ulrik Brandes,et al.  Network Analysis: Methodological Foundations , 2010 .

[44]  Darryl C. Draper.  Collaborative Instructional Strategies to Enhance Knowledge Convergence , 2015 .

[45]  Jan van Aalst,et al.  Assessment in collaborative learning , 2013 .

[46]  Ryan S. Baker,et al.  Merlot Journal of Online Learning and Teaching Content or Platform: Why Do Students Complete Moocs? , 2022 .

[47]  Brigid Barron.  Achieving Coordination in Collaborative Problem-Solving Groups , 2000 .

[48]  Allan Jeong,et al.  Effects of Pre‐structuring Discussion Threads on Group Interaction and Group Performance in Computer‐supported Collaborative Argumentation , 2006 .

[49]  Sara McNeil,et al.  Students' patterns of engagement and course performance in a Massive Open Online Course , 2016, Comput. Educ..

[50]  George Veletsianos,et al.  Digging deeper into learners' experiences in MOOCs: Participation in social networks outside of MOOCs, notetaking and contexts surrounding content consumption , 2015, Br. J. Educ. Technol..

[51]  Arthur C. Graesser,et al.  The relationship between cognitive disequilibrium, emotions and individual differences on student question generation , 2014, Int. J. Learn. Technol..

[52]  Gijsbert Erkens,et al.  Group awareness of social and cognitive performance in a CSCL environment: Effects of a peer feedback and reflection tool , 2011, Comput. Hum. Behav..

[53]  Allison Littlejohn,et al.  Instructional quality of Massive Open Online Courses (MOOCs) , 2015, Comput. Educ..

[54]  A. Sher.  Assessing the Relationship of Student-Instructor and Student-Student Interaction to Student Learning and Satisfaction in Web-Based Online Learning Environment , 2009 .

[55]  Amy E. Stich,et al.  Massive open online courses and underserved students in the United States , 2017, Internet High. Educ..

[56]  Charlotte N. Gunawardena,et al.  Analysis of a Global Online Debate and the Development of an Interaction Analysis Model for Examining Social Construction of Knowledge in Computer Conferencing , 1997 .

[57]  Stephanie D. Teasley,et al.  Democratizing Higher Education: Exploring MOOC Use Among Those Who Cannot Afford a Formal Education , 2014 .

[58]  D. Keegan,et al.  Three Types of Interaction , 2013 .

[59]  Frances Bell,et al.  Rhizo14: A Rhizomatic Learning cMOOC in Sunlight and in Shade. , 2015 .

[60]  James Harland,et al.  Evaluating the quality of interaction in asynchronous discussion forums in fully online courses , 2012 .

[61]  Patrick C. Shih,et al.  Understanding Student Motivation, Behaviors and Perceptions in MOOCs , 2015, CSCW.

[62]  George Siemens,et al.  Where is research on massive open online courses headed? A data analysis of the MOOC research initiative , 2014 .

[63]  D. Garrison,et al.  Facilitating Cognitive Presence in Online Learning: Interaction Is Not Enough , 2005 .

[64]  Sean P. Goggins,et al.  Connecting performance to social structure and pedagogy as a pathway to scaling learning analytics in MOOCs: an exploratory study , 2016, J. Comput. Assist. Learn..

[65]  Sean P. Goggins,et al.  A Context Awareness System for Online Learning: Design Based Research , 2009 .