TY - CHAP TI - Diverse Big Data and Randomized Field Experiments in Massive Open Online Courses AU - Kizilcec, Rene AU - Brooks, Christopher T2 - The Handbook of Learning Analytics A2 - Lang, Charles A2 - Siemens, George A2 - Wise, Alyssa Friend A2 - Gaševic, Dragan AB - 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. CY - Alberta, Canada DA - 2017/// PY - 2017 ET - 1 SP - 211 EP - 222 PB - Society for Learning Analytics Research (SoLAR) SN - 978-0-9952408-0-3 UR - http://solaresearch.org/hla-17/hla17-chapter1 ER -