Is your university making the most of its big data? Here’s how your team can adopt learning analytics.
Learning analytics is all about collecting, tracking, and analyzing the data around how people learn. This process allows educators to improve and optimize learning practices and environments.
In the higher education space, gathering and examining this data is a key priority for university staff, particularly for those in the institutional planning, strategy, and research office.
For these professionals, data-based decision making is the future and learning analytics is a core component of this approach.
So, what does this mean and how can university staff evolve?
The power of learning analytics
Collecting and examining university data for the purposes of identifying trends, areas of improvement, and insights empowers university staff to make decisions based on data, rather than on anecdotes or gut instincts.
By utilizing data in this way, strategic decisions can be made about the university’s future and the way it teaches and serves students.
For example, university staff could look at data around student and professor participation in online forums to gauge the efficiency of online forums when it comes to learning, fostering open debate, and encouraging participation, particularly for those students who don’t feel confident enough to speak up in lectures and group discussions.
On a larger level, university staff could analyze the data on student engagement in lectures and group discussions to inform specific course content and determine what resonates with students.
Adopting learning analytics
To introduce learning analytics at your university, you’ll first need to explore the available data and perform data cleansing, which involves removing any data that may unfairly skew results or negatively impact the value of the insights.
You then need to standardize any and all data processes so that each department understands how they collect their data, what data they need to share, and how they should share it with a central group or authority that looks after the data.
This central group will then own a ‘data lake’ that pools all the data to glean accurate insights and highlight any trends.
The next step is to build an initial model, establishing validation criteria, and confirming model objectives, ensuring that your learning analytics project is clearly defined.
This will detail what you want to learn and what insights you want to uncover, so you’re not just collecting and analyzing data for data’s sake.
It’s important to note that the indicators and criteria you examine will vary widely depending on the course or program you’re focusing on.
It’s important to continue to iterate, adding new variables and tweaking their weights as you progress throughout the project.
Remember to use visualization tools to help drive the process and incorporate feedback from end users in the final stages.
Finally, make sure you act on the insights you discover. The analytics need to be understood and actioned, as non-actionable analytics are useless.
To make the most of these projects, develop the relevant internal capabilities. Don’t just hire a data scientist and leave it at that, you need to develop internal competencies and invest in those skills.
It’s a strategic, long-term investment that your university needs to make.
To learn more about how the higher education sector is leveraging data and analytics, attend our 2020 EduData Summit, hosted at the United Nations in New York.