Every keyboard and touchscreen contributes to an ever-growing source of measurable data. Taming this data to extract value and better understand what it can mean for business is the new measure by which organisations can succeed or fail.
Data offers so much in the way of optimising a university and the moving parts that come with the university experience. From an administrative, through to academic or student experience perspective, it can play a big role.
If universities were able to scrutinise and manage the vast quantities of data being created every minute, all aspects of their important business – from cutting-edge research to workforce management – could be revolutionised, and leadership enabled to make better, more informed decisions.
Yet, the higher education sector isn’t moving as fast as it could to turn information into insights. Part of the reason for this is simply the deluge of data pouring in from so many different downpipes. Various faculties may be operating independently of one another, leaving information siloed in a multitude of formats across disparate systems that don’t speak to one another.
A challenge that’s faced not just by the education sector but by many businesses, too, is that the sheer volume of generated data can seem daunting. Earlier this year, a group of University of Melbourne academics admitted in a blog post that “curating this data in a way that allows educators and researchers real-time access for analysis is something most institutions are still grappling with.”¹
Deciding what to do with all this data presents further challenges in a university setting, where decisions have long been based on tradition, history and departmental preferences:
Internationally, there are some encouraging examples of how universities are using analytics to understand learning processes and tailor education to students’ individual needs.
Learning analytics builds a profile of the individual to provide insights that can influence teaching methods. Software automatically gathers information as students use devices; it collates marks, notes areas of strength or weakness, looks at keystrokes, and even measures moments of hesitation on a mouse.
With the help of such analysis, the teaching of maths at Arizona State University is fully customised – a system known as adaptive learning.
This made-to-measure learning allows teachers to support students individually. As maths professor Irene Bloom says, “I can see what’s causing [each student] difficulty and we’ll sit down and talk about those ideas”.²
Since embracing data analytics, Arizona University’s student success rate has improved and its dropout rates have decreased by 54%.
Closer to home, a 2015 report commissioned by the Australian Government Office for Learning and Teaching found that although there’s much interest in the potential of this new field to shape the future of education, learning analytics projects across Australian universities are mostly “immature and small in scale.”³
It’s not just student performance that can be measured.
Pivot is a Melbourne-based startup that’s focused on teacher performance in the classroom. In 2015 it launched an online tool, now used in over 150 schools in Australia, which allows students to provide feedback on teachers’ strengths and weaknesses. This delivers insights that can be used as a professional development tool.
An improvement in learning outcomes doesn’t have to start in the classroom, nor do the benefits only reach as far as student grades. Getting a better handle on student acquisition and retention means a better administrative and financial outcome for higher education providers.
Predictive analytics can look at a range of factors known to correlate with attendance in order to more accurately forecast intake and retention.
Data analysis at Washburn University in Kansas, in the US, revealed that students that work on campus are more likely to persist with their studies. The university then looked into how more students could be employed in meaningful ways on site and increasing the overall number of jobs available. A similar result was uncovered for students that live on campus, so it sought to increase the amount of on-site housing4.
Students that are late to fill out their enrolment forms, are unresponsive to tutor outreach, log a low number of contact hours, lack adequate funding or fail certain classes – all these factors may indicate a greater likelihood of dropping out.
Manchester Metropolitan University in the UK carried out analysis that even revealed a direct correlation between student retention and engagement indicators such as how frequently a student swiped into the library or how many books they borrowed5.
While such data as seen in these examples may have been gathered by universities for some time, it would most likely have been used for traditional reporting purposes. The impetus for education providers now is to track it while the data still lives. Predictive analytics can be responded to in real time, allowing active and personalised interventions that support current student outcomes and enables better forecasting for administrative departments.
Efforts by higher education institutions to use data to make better institutional decisions and improve operational efficiency can be viewed as a version of what the private sector calls ‘business intelligence’. Many educators shy away from using this term, perhaps because it has corporate undertones, opting instead for ‘institutional intelligence’, ‘academic intelligence’ or ‘operational intelligence’6.
Whatever the description, leveraging data in Australian universities is an intelligent thing to do, but it’s not something that will happen overnight. It requires a strategic program and investment. It has to be monitored. Accountability for its success should be established and implementation will require strong leadership.
In an increasingly complex and competitive environment, embracing data analytics could give universities and their students the competitive edge.
The higher education industry faces disruption across the whole of its value chain, meaning a greater need than ever to adapt. University data will not only provide the precision as to where efforts would be best placed, it can help institutions build the case for change and measure the success of its outcomes. Now is the time to make that move.
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