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Research Affiliate Christian Fisher (left) is lead author of an article in the Review of Research in Education analyzing the challenges and potential benefits of mining big data. The title of the article is "Mining big data in education: Affordances and challenges." Co-authors are Associate Professor Zachary Pardos (UC Berkeley), Associate Professor Ryan Shawn Baker (University of Pennsylvania), Assistant Professor Joseph Jay Williams (University of Toronto), Chancellor’s Professor Padhraic Smyth, fourth-year doctoral student Renzhe Yu (cener left), Data Scientist Stefan Slater (University of Pennsylvania), Assistant Professor Rachel Baker (center right) and Professor Mark Warschauer (right).
Fischer is Assistant Professor of Educational Effectiveness at the Hector Research Institute of Education Sciences and Psychology at the University of Tübingen in Germany and a Research Affiliate with the UC Irvine School of Education. Previously, he served as a distinguished postdoctoral scholar at UC Irvine working under the mentorship of Warschauer and Dean Richard Arum. Fischer received his Ph.D. in Learning Technologies from the University of Michigan
Abstract The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness. Digital traces of student behavior promise more scalable and finer-grained understanding and support of learning processes, which were previously too costly to obtain with traditional data sources and methodologies. This synthetic review describes the affordances and applications of microlevel (e.g., clickstream data), mesolevel (e.g., text data), and macrolevel (e.g., institutional data) big data. For instance, clickstream data are often used to operationalize and understand knowledge, cognitive strategies, and behavioral processes in order to personalize and enhance instruction and learning. Corpora of student writing are often analyzed with natural language processing techniques to relate linguistic features to cognitive, social, behavioral, and affective processes. Institutional data are often used to improve student and administrational decision making through course guidance systems and early-warning systems. Furthermore, this chapter outlines current challenges of accessing, analyzing, and using big data. Such challenges include balancing data privacy and protection with data sharing and research, training researchers in educational data science methodologies, and navigating the tensions between explanation and prediction. We argue that addressing these challenges is worthwhile given the potential benefits of mining big data in education. Comments are closed.
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