ABSTRACT
This chapter establishes the educational context for data analytics and adaptive learning by demonstrating similar learning objectives achieved with considerably different methods. The issues addressed include learning asymmetries, nontractable data, autocatalytic learning, big data, the three-body problem, criticism of analytics and adaptive learning, complexity in higher education, cloud computing, artificial intelligence and deep learning, resistance to change, and data-driven cultures. Data analytics has a long history in the corporate sector especially in customer relations management where companies realized that understanding client interests and buying preferences increased their conditional profit margins. Furthermore, universities all over the world are exploring analytics to improve student success. Adaptive learning, the second area addressed in this book, increases the odds of student success just as data analytics does. However, it approaches the risk problem in an alternative way. To understand the immensity of successfully transforming higher education, it is necessary to step back and conceptualize the three-body data problem within the context of the three institutional forces that are responsible for creating, maintaining, and governing the integration of big data, analytics, and adaptive learning. However, as societal demands on the Academy continue to increase, it is imperative that these forces be reconciled.
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