Data and analytics are being used across almost all aspects of our lives, reshaping how individuals and businesses alike make decisions about virtually everything. Data is helping airlines and hotels reward their most loyal customers with priority seating and added amenities. Data is determining what advertisement shows up in our mailbox and what campaign commercials we see on TV. Data even influenced the Oakland A's recruiting strategy when they determined which players would start strictly based on statistics, as depicted in the movie Moneyball.
Very few would argue that using an existing resource to improve efficiency and positively impact results is a bad idea. While some of the uses of data may not be welcomed by all, there are, inarguably, real opportunities to use data to positively impact many aspects of our lives. Higher Education is one of these opportunities. Large quantities of data exist currently within the higher education ecosystem but, like many other industries, we are just beginning to grasp the implications and learn how to effectively utilize it to improve the quality of higher learning in this country.
The challenge comes in harnessing existing data, often from multiple sources, and making it accessible and actionable in a way that is meaningful. In short, turning disparate pieces of data into analytics.
The most useful data is likely to be fragmented and held in multiple systems within an institution, from IT to administrators to individual professors. Even once the appropriate data is collected, it needs to be aggregated in a way that can answer important questions and generate actionable results. If done effectively, data can be used to help identify problem areas and ensure that among other things, resources are allocated appropriately, that learning outcomes for students are improved and that efficiencies are created within the university.
Each industry has to tackle this challenge in a way that is appropriate for their unique business. Education is no different and we are starting to see data turned into analytics inform the most critical parts of the higher education ecosystem.
Why do analytics in higher education matter?
According to the Department of Education, only 39.3 percent of adults 25-34 year olds in the U.S. have a post-secondary degree and of those who enroll in a higher education program, only about 55 percent graduate within six years. Tuition costs are now rising faster than healthcare, with student loan debt now totaling more than $1 trillion, a report from the Consumer Financial Protection Bureau's student loan ombudsman detailed. With such staggering numbers, we have an obligation to use every resource at our disposal to lower costs, ensure students graduate on time and make absolute certain that academic achievement remains high with our students prepared to compete on a global marketplace. Predictive analytics hold the promise that faculty and administrators can intervene at an earlier juncture and help positively impact learning outcomes that ultimately lead to higher graduation rates.
As tuition costs continue to increase, many students can't afford not to graduate on time. Further, many states are allocating funding based on graduation rates. For example, Tennessee began to tie more state funding to graduation rates in 2010 and government officials in Texas are advocating for 10 percent of a college's state appropriation to be tied to the graduation rate. Florida is also jumping on board with plans to tie funding to performance starting in 2013, all in an effort to improve graduation rates.
For these reasons, improving graduation rates is in the financial interest of both the student and the institution. To address this, some institutions have begun to use data and analytics to determine problem areas and identify "at risk" students who are veering off the graduation path. Purdue University's Course Signals, for example, detects early warning signs of failure and provides both the student who may not be performing to the best of their ability and faculty member with intervention communication before they reach a critical tipping point of no return. Austin Peay State University also built software called Degree Compass, which uses predictive analytics to forecast and ensure students are successful in each course they take.
Understanding the Non-Traditional Student
Institutions aim for students to be successful from day one and to ensure the appropriate resources are available, institutions need to better understand their student population and grasp the learning impact of diverse backgrounds. This is especially true with non-traditional classroom settings including both online and hybrid models. One effort currently underway is a data-mining project called Predictive Analytics Reporting (PAR) Framework from the Bill and Melinda Gates Foundation, WCET and the WICHE Cooperative for Educational Technologies, which uses student performance data from six different universities and pulls it into one aggregated and anonymous database. Critical factors of success can then be compared side-by-side to better understand what students need to be successful.
Perhaps the biggest opportunity that data provides in higher education is to improve learning outcomes to help ensure academic achievement. There are a variety of ways that faculty are using analytics at the classroom level. A Harvard professor, for example, is using a classroom management technology to pull together and analyze student performance data to more appropriately assign class discussion partners. The data is used to identify certain students that do not understand concepts, which can be particularly helpful when teaching large lecture courses. Additionally, a variety of products from all major publishers now integrate automated assessment tools into the curriculum, which provide personalized feedback and even suggest concepts that the student should go back and review in an effort to ensure concept mastery.
The Future of Data in Higher Ed
As in other areas of our lives, the use of data and analytics has the potential to greatly improve higher education, if we are able to effectively navigate it. In a recent webinar, the Center for American Progress said it's not enough to simply have data collected, it is most useful when a mission is set and then data is used to further that mission.
Whether that mission is to increase degree completion rates, reduce costs, increase efficiency within the university, improve the quality of education or all of the above, the amount of information we are able to meaningfully analyze will unquestionably continue to improve the higher education ecosystem.