Handbook of Learning Analytics
Diverse Big Data and Randomized
Field Experiments in MOOCs
René F. Kizilcec & Christopher Brooks
A new mechanism for delivering educational content at large scale, massive open online courses (MOOCs), has attracted millions of learners worldwide. Following a concise account of the recent history of MOOCs, this chapter focuses on their potential as a research instrument. We identify two critical affordances of this environment for advancing research on learning analytics and the science of learning more broadly. The first affordance is the availability of diverse big data in education. Research with heterogeneous samples of learners can advance a more inclusive science of learning, one that better accounts for people from traditionally underrepresented demographic and sociocultural groups in more narrowly obtained educational datasets. The second affordance is the ability to conduct large-scale field experiments at minimal cost. Researchers can quickly evaluate multiple theory-based interventions and draw causal conclusions about their efficacy in an authentic learning environment. Together, diverse big data and experimentation provide evidence on “what works for whom” that can extend theories to account for individual differences and support efforts to effectively target materials and support structures in online learning environments.
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Diverse Big Data and Randomized Field Experiments in MOOCs
Handbook of Learning Analytics
Society for Learning Analytics Research
1. Department of Communication, Stanford University, USA
2. School of Information, University of Michigan, USA
3. Teachers College, Columbia University, USA
4. LINK Research Lab, University of Texas at Arlington, USA
5. Learning Analytics Research Network, New York University, USA
6. Schools of Education and Informatics, University of Edinburgh, UK