Who are the top contributors in a MOOC? Relating participants' performance and contributions

The role of social tools in massive open online courses MOOCs is essential as they connect participants. Of all the participants in an MOOC, top contributors are the ones who more actively contribute via social tools. This article analyses and reports empirical data from five different social tools pertaining to an actual MOOC to characterize top contributors and provide some insights aimed at facilitating their early detection. The results of this analysis show that top contributors have better final scores than the rest. In addition, there is a moderate positive correlation between participants' overall performance measured in terms of final scores and the number of posts submitted to the five social tools. This article also studies the effect of participants' gender and scores as factors that can be used for the early detection of top contributors. The analysis shows that gender is not a good predictor and that taking the scores of the first assessment activities of each type test and peer assessment in the case study results in a prediction that is not substantially improved by adding subsequent activities. Finally, better predictions based on scores are obtained for aggregate contributions in the five social tools than for individual contributions in each social tool.

[1]  Robert Plonsey,et al.  Bioelectricity: A Quantitative Approach Duke University’s First MOOC , 2013 .

[2]  Judy Kay,et al.  Clustering and Sequential Pattern Mining of Online Collaborative Learning Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

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

[4]  L. Chidambaram,et al.  Emergent Leadership in Self-Managed Virtual Teams , 2006 .

[5]  Dirk T. Tempelaar,et al.  The role of academic motivation in Computer-Supported Collaborative Learning , 2009, Comput. Hum. Behav..

[6]  George Siemens,et al.  Penetrating the fog: analytics in learning and education , 2014 .

[7]  George Siemens,et al.  Learning analytics: envisioning a research discipline and a domain of practice , 2012, LAK.

[8]  Nishikant Sonwalkar,et al.  The First Adaptive MOOC: A Case Study on Pedagogy Framework and Scalable Cloud Architecture—Part I , 2013 .

[9]  George Siemens,et al.  The MOOC model for digital practice , 2010 .

[10]  Chris Piech,et al.  Deconstructing disengagement: analyzing learner subpopulations in massive open online courses , 2013, LAK '13.

[11]  Ryan S. Baker,et al.  The State of Educational Data Mining in 2009: A Review and Future Visions. , 2009, EDM 2009.

[12]  Carlos Delgado Kloos,et al.  Towards the Prediction of User Actions on Exercises with Hints Based on Survey Results , 2011, EC-TEL.

[13]  Paul Hyman.  In the year of disruptive education , 2012, CACM.

[14]  Jan-Willem Strijbos,et al.  Developing the role concept for computer-supported collaborative learning: An explorative synthesis , 2010, Comput. Hum. Behav..

[15]  Mykola Pechenizkiy,et al.  Handbook of Educational Data Mining , 2010 .

[16]  Justin Reich,et al.  HarvardX and MITx: The First Year of Open Online Courses, Fall 2012-Summer 2013 , 2014 .

[17]  Carlos Delgado Kloos,et al.  Inferring higher level learning information from low level data for the Khan Academy platform , 2013, LAK '13.

[18]  Shane Dawson,et al.  Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan , 2012, J. Educ. Technol. Soc..

[19]  Christoph Meinel,et al.  Designing MOOCs for the Support of Multiple Learning Styles , 2013, EC-TEL.

[20]  Justin Cheng,et al.  Tools for predicting drop-off in large online classes , 2013, CSCW '13.

[21]  Carlos Delgado Kloos,et al.  Motivation and Emotions in Competition Systems for Education: An Empirical Study , 2014, IEEE Trans. Educ..

[22]  P Malliga,et al.  A Survey On MOOC Providers For Higher Education , 2013 .

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

[24]  Agustín C. Caminero,et al.  Analyzing the students' behavior and relevant topics in virtual learning communities , 2014, Comput. Hum. Behav..

[25]  C. Chinn,et al.  Innovating Pedagogy 2017: Exploring new forms of teaching, learning and assessment, to guide educators and policy makers. Open University Innovation Report 6 , 2017 .

[26]  Carlos Delgado Kloos,et al.  An architecture for extending the learning analytics support in the Khan Academy framework , 2013, TEEM '13.

[27]  Neil T. Heffernan,et al.  Predicting State Test Scores Better with Intelligent Tutoring Systems: Developing Metrics to Measure Assistance Required , 2006, Intelligent Tutoring Systems.

[28]  Carlos Delgado Kloos,et al.  Delving into Participants’ Profiles and Use of Social Tools in MOOCs , 2014, IEEE Transactions on Learning Technologies.

[29]  Rebecca Ferguson,et al.  Social Learning Analytics , 2012, J. Educ. Technol. Soc..

[30]  I. Arroyo,et al.  Bayesian networks and linear regression models of students’ goals, moods, and emotions , 2010 .

[31]  Janice D. Gobert,et al.  From Log Files to Assessment Metrics: Measuring Students' Science Inquiry Skills Using Educational Data Mining , 2013, Journal of the Learning Sciences.

[32]  Paul Hyman.  In honor of Alan Turing , 2012, CACM.

[33]  Abdellatif Medouri,et al.  LASyM: A Learning Analytics System for MOOCs , 2013 .

[34]  Maarten de Laat,et al.  Social Learning Analytics applied in a MOOC-environment , 2014 .

[35]  John D. Burger,et al.  Discriminating Gender on Twitter , 2011, EMNLP.

[36]  Shane Dawson,et al.  'Seeing' the learning community: An exploration of the development of a resource for monitoring online student networking , 2010, Br. J. Educ. Technol..

[37]  George Siemens,et al.  Learning analytics and educational data mining: towards communication and collaboration , 2012, LAK.

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