Do educators need to become data scientists?
Australia is lucky to have high quality and consistent data standards underpinning tertiary education.
That’s because when you’re looking at doing things with data - gaining value from it - having consistent data models helps; for example, ReadyTech has data going back historically with some education clients over a decade.
You can do pretty amazing things with that - it’s a major opportunity for customers and the sector.
The question is this: as a sector, are we skilled enough to make the best use of the data available?
The case for data skills in education
Educators don’t all need to be data scientists. Most people get into the education business because they want to give the gift of their own knowledge and skills to students, to contribute to their success in work and life.
They’re passionate about their subjects and niches and want to share that passion with others.
They’re not in it to sit there crunching numbers.
But it seems that educators over time will need to become closer to masters in the discipline of data.
Why? Because while the traditional skill sets and qualities needed by education providers will remain, we can see how an understanding of data will become important to augmenting these. This is true from a business perspective, but also when thinking about things like student experience, satisfaction, learning, wellness and completion.
And if we’re honest about lifelong learning, that needs to include education professionals too.
We should all be aware of – and potentially upskilling in – those adjacent skills areas that will support the conduct of better education offerings into the future. If we have a skills deficit in Australian tertiary education around data and what to do with it, then we probably need to address that problem to improve our offerings.
Levelling up our data understanding
There are many instances where we could be surfacing and applying data more efficiently and effectively to improve an education business or the student journey. But often, because it is not in the forefront of business as usual for educators, the opportunities to utilise it are foregone or overlooked. Here’s just two of these.
Business intelligence and reporting
A lot of educators still think about using data as pulling reports from a system. According to this line of thinking, it’s about periodically looking at historical data, usually at mid-to-senior management level, to ascertain what’s going on, and to make adjustments to practices and processes based on what is gleaned from those reports.
This is all well and good. Pulling and analysing reports can be valuable. It can even be fun for some!
But we’d also say that, given trends in how data can be used, this approach is becoming outdated. As a tech company, we see data as something that lives within a system every day, and supports decision making at all levels among different users. Ideally, it’s not a static artifact that is downloaded and parsed after the fact.
We seek to capitalise on this new data paradigm when we go about designing tech products. We need to ask ourselves things like; 'How we can surface valuable data in intuitive and visually engaging ways within existing workflows to empower users - from teachers to CEOs - to do their jobs faster and better?'.
With a data-led approach you’re presenting users with data as you go through the process of work itself.
For example, in another part of our business, we’re undertaking a data science project that provides a real-time drop-out risk rating for Australian apprentices. It’s based on a number of factors that affect apprenticeships completion, from distance from learning or a work site, to their individual motivation for learning.
This data doesn’t need to be extracted into list form by an apprenticeship support provider. Instead, if a system is designed well, the data lives within the system and the system will surface that and recommend action in interactions with apprentices. It is delivered in such a way that it activates frontline users to give a response in real-time.
In the realm of apprentice drop-outs – or students for that matter – this real-time access and prompting based on data can make all the difference. After all, if a student enters a risk category and you intervene late, the opportunity to turn the situation around is lower. With real-time data, you don’t need to wait for a report to come out.
Learning technology and experience
Another example where data can be put to work better would be in the learning space.
One of our senior execs here at ReadyTech recently took on a course of study at a fairly prestigious university in the UK (yes, we do take lifelong learning seriously!). But, being a consummate education consumer across various platforms - including those providing seriously good online learning - this university’s offering appeared outdated.
With the likes of LinkedIn Learning, Udemy and others providing personalised and adaptive learning, our exec was left wondering why the university wasn’t able to do things like circle back to areas he didn’t do well in, provide more high quality online learning production or serve up related learning or opportunities in ways tech platforms do.
With learning data, educators - whether they be global universities, or vocational providers in Australia – have the power to create more personalised offerings. For example, ReadyTech’s aNewSpring LMS utilises both personalisation and adaptive learning features, and is available for VET and other Australian education markets right now.
Becoming education data science pros
It’s incumbent on us as a technology provider to serve our customers up products that enable them to utilise data well. However, we see that over time, providers will need to be building more skills in this area themselves, so that they are able to create the best possible offering for their students.
The solution can only be an embrace of professional development (as well as a willingness to conceptualise that in the broadest possible terms). In the education sector we are well aware of the value of lifelong learning. We also need to take that on board ourselves as career education professionals.
This means PD will need to evolve. Rather than the standard subjects we're all used to, the future will need to see the likes of data visualisation and analytics become disciplines that educators should expect to see at industry conferences. While we won’t all be data scientists, we’ll know a lot more about data - and what to do with it.
Interested in learning more about how we help providers with next generation enterprise student management technology? Learn more here.