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Problems in Big Data Analytics in Learning

When the National Academy of Engineering issued its grand challenges – specifically the one on “advancing personalized learning” (National Academy of Engineering, 2008) – it called for the development of new instrumentation, tools, and methodologies to bring learning closer to the learners and their personal choices. Big data plays a critical role in beginning to meet this grand challenge. A tremendous amount of data on students’ learning and behavioral experiences is captured in a wide variety of institutional systems. These data range from student demographics and socio-economic backgrounds to data about academic progress and extend down to individual mouse clicks when students are accessing course materials or their time spent viewing a screen of course information. Data captured from automated software-based learning environments in combination with more traditional forms of educational data provide a unique opportunity to understand how learning occurs and to engineer these processes in unprecedented ways. Data by themselves have only limited value. True transformation of educational ecosystems lies in converting these data into actionable intelligence (meaning insights and knowledge that enable learners and other stakeholders to act). Learning data vary significantly in modality (such as visual, auditory, and tactile) and dimensionality (range of observed characteristics). More important, the contexts from which these data are derived may vary enormously. It is this unique combination of factors that makes big data in learning such an interesting research artifact. The ability to couple data about learners (actors or agents) and the system structure (courses, schools, university, or industry settings) within which they function is powerful. Many proponents of big data in learning believe that these data, considered as a single unified ecosystem, could eventually uncover the distributed nature of human cognition that is embedded in a thick network of human behaviors. The ability to shed light onto the so-called “ghost in the machine” (Koestler, 1967), where thoughts are embodied in learner actions, has a powerful appeal. In this guest editorial, we discuss the notion of big data and its potential for transforming learning and educational ecosystems.