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Preface

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Workshop on Massive Open Online Courses (moocshop)

Workshop Co-Chairs:

Zachary A. Pardos

Massachusetts Institute of Technology

Emily Schneider Stanford University

http://www.moocshop.org

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Preface

The moocshop surveys the rapidly expanding ecosystem of Massive Open Online Courses (MOOCs). Since late 2011, when enrolment for Stanford’s AI class went viral, MOOCs have been a compelling and controversial topic for university faculty and administrators, as well as the media and blogosphere. Research, however, has played a relatively small role in the dialogue about MOOCs thus far, for two reasons. The first is the quickly moving landscape, with course scale and scope as the primary drivers for many stakeholders. The second is that there has yet to develop a centralized space where researchers, technologists, and course designers can share their findings or come to consensus on approaches for making sense of these emergent virtual learning environments.

Enter the moocshop. Designed to foster cross-institutional and cross-platform dialogue, the moocshop aims to develop a shared foundation for an interdisciplinary field of inquiry moving forward. Towards this end, we invited researchers, technologists, and course designers from universities and industry to share their work on key topics, from analytics to pedagogy to privacy. Since the forms and functions of MOOCs are continuing to evolve, the moocshop welcomed submissions on a variety of modalities of open online learning. Among the accepted papers and abstract-only submissions, four broad categories emerged:

· Position papers that proposed lenses for analyses or data infrastructure required to lower the barriers for research on MOOCs

· Exploratory analyses towards designing tools to assess and provide feedback on learner knowledge and performance

· Exploratory analyses and case studies characterizing learner engagement with MOOCs

· Experiments intended to personalize the learner experience or affect the psychological state of the learner

These papers and abstracts are an initial foray into what will be an ongoing dialogue, including discussions at the workshop and a synthesis paper to follow based on these discussions and the proceedings. We are pleased to launch the moocshop at the joint workshop day for AIED and EDM in order to draw on the expertise of both communities and ground the workshop discussions in principles and lessons learned from the long community heritage in educational technology research. Future instantiations of the moocshop will solicit contributions from a variety of different conferences in order to reflect the broad, interdisciplinary nature of the MOOC space.

June, 2013 Zachary A. Pardos & Emily Schneider

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Program Committee

Co-Chair: Zachary A. Pardos, MIT, USA ([email protected]) Co-Chair: Emily Schneider, Stanford, USA ([email protected])

Ryan Baker, Columbia Teacher's College, USA Amy Collier, Stanford University, USA Chuong Do, Coursera, USA

Neil Heffernan, Worcester Polytechnic Institute, USA Jack Mostow, Carnegie Mellon University, USA

Una-May O'Reilly, Massachusetts Institute of Technology, USA Zach Pardos, Massachusetts Institute of Technology, USA David Pritchard, Massachusetts Institute of Technology, USA Emily Schneider, Stanford University, USA

George Siemens, Athabasca University, USA John Stamper, Carnegie Mellon University, USA

Kalyan Veeramachaneni - Massachusetts Institute of Technology, USA

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Table of Contents

Position / Overview

Two Models of Learning: Cognition Vs. Socialization 1 Shreeharsh Kelkar

Welcome to the moocspace: a proposed theory and taxonomy for massive 2 open online courses

Emily Schneider

Roll Call: Taking a Census of MOOC Student 10

Betsy Williams

Developing Data Standards and Systems for MOOC Data Science 17 Kalyan Veeramachaneni, Franck Dernoncourt, Colin Taylor,

Zachary Pardos and Una-May O'Reilly Assessment

Syntactic and Functional Variability of a Million Code Submissions 25 in a Machine Learning MOOC

Jonathan Huang, Chris Piech, Andy Nguyen and Leonidas Guibas

Revisiting and Extending the Item Difficulty Effect Model 33 Sarah Schultz and Trenton Tabor

Using Argument Diagramming to Improve Peer Grading of 41 Writing Assignments

Mohammad H. Falakmasir, Kevin D. Ashley and Christian D. Schunn Interventions

Improving learning in MOOCs with Cognitive Science 49

Joseph Jay Williams

Measurably Increasing Motivation in MOOCs 55

Joseph Jay Williams, Dave Paunesku, Benjamin Heley and Jascha Sohl-Dickstein

Controlled experiments on millions of students to personalize learning 56 Eliana Feasley, Chris Klaiber, James Irwin, Jace Kohlmeier

and Jascha Sohl-Dickstein

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Engagement

Analysis of video use in edX courses 57

Daniel T. Seaton, Albert J. Rodenius, Cody A. Coleman, David E. Pritchard and Isaac Chuang

Exploring Possible Reasons behind Low Student Retention Rates of 58 Massive Online Open Courses: A Comparative Case Study from a

Social Cognitive Perspective Yuan Wang

Using EEG to Improve Massive Open Online Courses Feedback Interaction 59 Haohan Wang, Yiwei Li, Xiaobo Hu, Yucong Yang, Zhu Meng

and Kai-Min Chang

Collaborative Learning in Geographically Distributed and In-person Groups 67 Rene Kizilcec

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