Data Mining and Learning Analytics

CHAPTER 4EDUCATIONAL DATA MINING: A MOOC EXPERIENCE

Ryan S. Baker¹, Yuan Wang¹, Luc Paquette², Vincent Aleven³, Octav Popescu³, Jonathan Sewall³, Carolyn Rosé³, Gaurav Singh Tomar³, Oliver Ferschke³, Jing Zhang⁴, Michael J. Cennamo⁴, Stephanie Ogden⁴, Therese Condit⁴, José Diaz⁴, Scott Crossley⁵, Danielle S. McNamara⁶, Denise K. Comer⁷, Collin F. Lynch⁸, Rebecca Brown⁸, Tiffany Barnes⁸, and Yoav Bergner⁹

¹ Department of Human Development and Department of Curriculum & Instruction Teachers College, Columbia University, New York, NY, USA

² Human‐Computer Interaction Institute, Department of Mathematics, Science, and Technology, University of Illinois Urbana‐Champaign, Champaign, IL, USA

³ Center for Teaching & Learning, Department of Applied Linguistics & ESL, Learning Sciences Institute, Thompson Writing Program, Carnegie Mellon University, Pittsburgh, PA, USA

⁴ Department of Computer Science, Computational Psychometrics Research College, Columbia University, New York, NY, USA

⁵ Georgia State University, Atlanta, GA, USA

⁶ Arizona State University, Tempe, AZ, USA

⁷ Duke University, Durham, NC, USA

⁸ North Carolina State University, Raleigh, NC, USA

⁹ Educational Testing Service, Princeton, NJ, USA

In this chapter, we describe a MOOC on educational data mining (EDM)/learning analytics, Big Data in Education (referred to later as BDEMOOC in some cases). We will describe BDEMOOC’s goals, its design and pedagogy, its content, and the research it afforded.

4.1 BIG DATA IN EDUCATION: ...