Universities Need To Use Analytics Better

Despite being knowledge centres, they need to know more about students


Organizations are failing with Big Data. That’s according to recent pan-European research examining the impact that Big Data has on organizations, which found that more than 50% of businesses have missed out on around $29m a year on average due to a lack of accurate information when they really needed it. One of the primary reasons for this is the current lack of candidates with the skills to understand and leverage data. In an attempt to correct this, universities like NYU and Michigan state have set up specialist analytics courses. Just a decade ago, a handful of colleges in the US offered Big Data/analytics degree programs. Now almost 100 schools have data-related undergraduate and graduate degrees, as well as certificates for working professional or graduate students wanting to augment other degrees.

Given this, it is somewhat ironic that a newly-released inquiry report by the Higher Education Commission, ‘From Bricks to Clicks: The potential of data and analytics, in higher education’, has found that universities are among the very organizations failing to use Big Data to its full potential. Indeed, a 2015 survey conducted by the Heads of E-Learning Forum in the UK found that nearly half of higher education institutions had implemented no learning analytics at all.

Students now leave vast digital footprints across their campuses and virtual learning environments. Colleges and universities can leverage a tremendous amount of this to drive performance as well as student engagement, which will ultimately help to boost retention rates - vital in the US because of the billions that states waste on grants for students who eventually dropout. Many have already started to try and harness the data they hold on virtual learning environments, attendance, library use, assignment submission and grades to drive more data-driven decision making. But there is still clearly some way to go.

Its applications are many. Data analytics can help quickly identify students in crisis by providing constant feedback on their performance in comparison to their their targets and how well their peers are doing. This enables staff to put measures in place to help them before the problem escalates. It can even help get a jump on falling grades by looking at whether students are doing things like checking into the library and withdrawing books, and compare them with past student data to pinpoint indicators that a student is going off the rails.

Big Data technology is also being used to improve the education experience itself, and provide a more interactive learning experience. Lecturers often receive minimal feedback on their teaching until their students either graduate or fail, by which point it is too late to adapt their methods. A Michigan University professor has created LectureTools in response to this problem. LectureTools lets students follow lecture presentations on their laptops, annotating them as they go along. The software also allows them to ask questions anonymously during the lecture, for those too embarrassed to ask out loud. The data from this tool can then be used by lecturers to help them engage with students that need individual attention and in seminars.

In 2016, we are likely to see universities go further to creating the kind of personalized Amazon-type experience that modern students expect from everyday life, with recommendation engines for things like courses and societies. This is being boosted by a number of government initiatives, such as the Obama Administration’s First in the World competition, in which some of the US’s largest universities tested the impact of data-informed advising on student outcomes. The results of this, released early in the year, should provide evidence to those not using data that they need to start.

To really use Big Data effectively, universities need to ensure that their own workforces are more data literate, perhaps even by looking to their own students. They also need to set aside competition and embrace collaboration with one another allowing their data to scale up, and look to other best-in-class institutions. Of course, some of the delay in adoption is due to concerns around privacy and data security, which is a fear particularly pronounced when it comes to young people. Universities need to be transparent in how they are using data, and promote the benefits. Students now pay such exorbitant fees that they need to feel they are getting value for money, and most would agree to a certain level of trade-off between their privacy and a tool that ensures they are receiving a premium learning experience. 


comments powered byDisqus
Bottom of well small

Read next:

Data Mining In The Deep Web