Penetrating the Fog: Analytics in Learning and Education.
EDUCAUSE review, 46 (5):
30 (2011)Abstract
Attempts to imagine the future of education often emphasize new technologies—ubiquitous computing devices, flexible classroom designs, and innovative visual displays. But the most dramatic factor shaping the future of higher education is something that we can’t actually touch or see: big data and analytics. Basing decisions on data and evidence seems stunningly obvious, and indeed, research indicates that data-driven decision-making improves organizational output and productivity.1 For many leaders in higher education, however, experience and “gut instinct” have a stronger pull.
Meanwhile, the move toward using data and evidence to make decisions is transforming other fields. Notable is the shift from clinical practice to evidence-based medicine in health care. The former relies on individual physicians basing their treatment decisions on their personal experience with earlier patient cases.2 The latter is about carefully designed data collection that builds up evidence on which clinical decisions are based. Medicine is looking even further toward computational modeling by using analytics to answer the simple question “who will get sick?” and then acting on those predictions to assist individuals in making lifestyle or health changes.3Insurance companies also are turning to predictive modeling to determine high-risk customers. Effective data analysis can produce insight into how lifestyle choices and personal health habits affect long-term risks.4 Business and governments too are jumping on the analytics and data-driven decision-making trends, in the form of “business intelligence.”
Higher education, a field that gathers an astonishing array of data about its “customers,” has traditionally been inefficient in its data use, often operating with substantial delays in analyzing readily evident data and feedback. Evaluating student dropouts on an annual basis leaves gaping holes of delayed action and opportunities for intervention. Organizational processes—such as planning and resource allocation—often fail to utilize large amounts of data on effective learning practices, student profiles, and needed interventions.
Something must change. For decades, calls have been made for reform in the efficiency and quality of higher education. Now, with the Internet, mobile technologies, and open education, these calls are gaining a new level of urgency. Compounding this technological and social change, prominent investors and businesspeople are questioning the time and monetary value of higher education.5 Unfortunately, the crescendo of calls for higher education reform lacks a foundation for making decisions on what to do or how to guide change. It is here—as a framework for making learning-based reform decisions—that analytics will have the largest impact on higher education.