@incollection{kizilcec_diverse_2017, address = {Alberta, Canada}, edition = {1}, title = {Diverse {Big} {Data} and {Randomized} {Field} {Experiments} in {Massive} {Open} {Online} {Courses}}, isbn = {978-0-9952408-0-3}, url = {http://solaresearch.org/hla-17/hla17-chapter1}, abstract = {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 in- strument. We identify two critical affordances of this environment for advancing research on learning analytics and the science of learning more broadly. The rst 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 eld experiments at minimal cost. Researchers can quickly evaluate multiple theory-based interventions and draw causal conclusions about their ef cacy 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.}, booktitle = {The {Handbook} of {Learning} {Analytics}}, publisher = {Society for Learning Analytics Research (SoLAR)}, author = {Kizilcec, Rene and Brooks, Christopher}, editor = {Lang, Charles and Siemens, George and Wise, Alyssa Friend and Gaševic, Dragan}, year = {2017}, pages = {211--222} }