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On the Horizon: The Power and Potential of Analytics

 “We are at the dawn of an era in which educators have the potential to harness technology to produce a dramatic change in student achievement.  Although visionaries have been promising for years that technology would transform primary and secondary education – and despite the billions of dollars spent on networking schools and equipping them with computers and other devices – the actual impact on student outcomes to date has been disappointing. Yet when technology is strategically introduced into every step of the educational value chain, it does, in fact have the potential to enhance every aspect of instruction and learning.” From Unleashing the Potential of Technology in Education. In order to dramatically improve student outcomes, technology must be fully aligned with educational objectives, standards, curricula, assessments, interventions and professional development.

The pathway to finding ways to optimize student success requires that leaders claim the mandate to expand access, quality and affordability.  The power and potential of analytics can provide more sustainable learning environments that encompass these three parts of the learning success model.  John Campbell and I point out in the Game Changers that “Analytics will be an essential future part of higher education. Institutions’ previous efforts of capturing data, providing availability in data warehouses, and initial data mining efforts are foundational to the next generation of activities. Higher education is benefiting from the extensive business intelligence efforts found in the corporate world and will develop new integrated solutions within the learning environment as one takes advantage of the LMS, SIS, and other emerging tools.”

Yet, higher education is at a critical juncture.  The Big Data: The Next Frontier for Innovation, Competition and Productivity report by McKinsey points out that higher education is at a very weak level among industry sectors in the overall ease of data capture, talent, IT intensity, data-driven mind set and overall data availability.  It is time to close the analytics gap.  This will require systematic building of organizational capacity.  Components include the technology infrastructure, tools and applications; policies, processes and practices; skills of faculty, staff, students and other stakeholders; culture and behaviors; and leadership at the institutional level. Not only is the improvement of institutional productivity at stake but also the capacity for long term sustainability.

What is on the horizon for analytics?  In the Game Changers, John Campbell and I suggest that the future of analytics promises to be both a sustaining and disruptive innovation for education. “Analytics as a sustaining innovation refers to the normal upgrading and integration of analytics into current teaching and learning tools. Today, institutions can implement a variety of analytics solutions as part of the course management and student information systems. Analytics as a sustaining innovation will serve higher education by providing incremental improvements in the existing system, while not widely disrupting the institutional processes. An example of a sustaining innovation is using predictive analytics to identify at-risk students early so institutions can intervene in a timely manner to increase the likelihood of success.”

In the Innovators Dilemma, Christensen describes disruptive innovation as a process of transformation affecting industries whose products and services are complicated, expensive, and inaccessible.  Disruptive innovations transform such products and services into simple, affordable, and convenient alternatives.  The more disruptive realm of analytics actions in higher education, include providing new products, ideas, activities, or interventions that require changing behavior/processes or modifying other products/services.  Analytics in this form breaks with current practice to serve the student, faculty, and administrative users in radically different ways; it serves new populations (or serves an existing population in radically different ways) and, in so doing, creates entirely new systems to accomplish this.

Several new disruptive innovations are emerging as a result of the move from metrics to analytics.  These include many initiatives that are being designed and implemented among the most innovative leaders in the higher education field.  Here are a few examples of where high performing institutions are now and where they aspire to be in the future.


• Utilizing “social” data to better understand student integration into campus. Research has found that environmental factors are equally as important as academic factors in student retention. How a student integrates into the social fabric, the formation of friendships and support groups, the adjustment into student housing, and similar factors all play an important role in student success. As the use of social media continues to increase, one could imagine mapping social connections to determine which students are having difficulty with connecting to the institution. Collecting, analyzing, and acting upon such data could potentially bring new groups together, ranging from housing, advising, and student groups.


• The growth of CRM as a collection point. Traditionally the “customer relationship management” (CRM) system has been focused on the admissions process. One could imagine future analytical tools coming together in a “learning relationship management” (LRM) system that would be open to faculty and advisors. The system would not only provide a central point for analytics data, but would also provide a way of tracking interventions and related results. The LRM system would provide a comprehensive foundation for end-to-end student support.


• Emergence of adaptive learning. If efforts to use analytics to predict success proved fruitful, the next significant step would be to use analytics to power adaptive systems that adapt to the learner’s needs based on behaviors of the individual as well as of past students’ patterns.


• Disaggregation of the data sources and the emergence of new analytics techniques. Analytics has focused primarily on integrating techniques into the course management and student information systems. When data from many different sources can be integrated, including audience response systems, publisher content, social media, and other data, new innovations will be possible.


• Mapping to interventions. Analytics can link suggested interventions to the use and impact of the interventions. If the intervention suggested utilizing the “math help desk,” did the student use the resource? If so, for how long and while doing what activities? To enable such mapping, new systems must be established to share data between organizations to ensure privacy, while still allowing for impact.” Baer and Campbell in Game Changers.


We are at a crossroads where we can, as described by Marc Parry in College Degrees, Designed by the Numbers, “massively change the likelihood of graduating” for students through a comprehensive analytical capacity that takes student success building blocks as the key performance indicators.  We are able to better tailor and customize instruction to the individual learner.  We will be able to use adaptive technologies to better guide students on successful academic pathways.  Don Norris and I have been working on a toolkit for building organizational capacity in analytics.  We have determined that institutions can better manage the student pipeline; eliminate impediments to retention and student success; utilize dynamic, predictive analytics to respond to at-risk student behavior; evolve learner relationship management systems; create personalized learning environments; engage in large-scale data mining to illuminate pathways to student success; and we can extend student success to include learning, workforce and life success.  This is what is on the horizon in the power and potential of analytics.