Taking the next step with data: It’s the smart thing to do

Today’s higher education leaders face many challenges, including around the role of data analytics as the industry continues to navigate how to gather, measure and intelligently analyse information to support decision-making within higher education institutions.

We know that data analytics has the potential to transform student experiences, creating actionable insights from reliable, privacy-compliant data. But how can higher education leaders get the most out of the data captured through the technology that supports their institution?

By incorporating intelligent data analytics into their teaching and learning practices and day-to-day operations, institutions have the potential to grow student enrolment, transform learner experiences and significantly improve student retention and completion rates. It can also help faculty innovate around existing pedagogical methods while improving research and output.

However, the real challenge for most higher education institution leaders lies in genuinely understanding how to get the most out of education technology (edtech) data analytics and the power of the raw data beyond standard reporting.

Taking the next step with data will create more intelligence-driven experiences to improve student outcomes and support instructors and administrators. But what does that next step look like? Is it investing in third-party technology or developing in-house analytical abilities to integrate data and inform decision-making within the institution? Or a hybrid of both?

Different audiences

Change through intelligent analytics has an added layer of complexity compounded by training and governance. I always suggest that colleges and universities start with the mindset that different audiences need different types of training and support before they move forward with anything.

For example, students need training if the institution is considering giving them direct access to more data. This can make a huge impact, as employing technology that can act like an invisible concierge to automate processes and support improved progress on their educational journey is the new norm.

For instructors and advisors, training should focus on understanding how to use data to identify students in need and adjust pedagogy or design appropriate interventions to help them be more successful.

Training for staff and administrators should cover leveraging aggregate outputs to effect positive change for entire cohorts, programmes or services.

But perhaps out of all the audiences, institutional researchers are one of the most important audiences in need of more training investment, particularly around helping researchers become more advanced at complex hypothesis testing.

Recognising bad habits

There is tremendous value in working with data analysts who can bring together the institutional ecosystem of data. Still, we find that institutional staff often fall back on old habits of running reports within silos or for the specific individual’s needs rather than exploring hypotheses where synthesising across datasets could be valuable.

For example, more people on campus – whether instructors, staff or administrators – should be saying things like: “We think that the students who most need support are those who are... [attribute from data set X], and also... [attribute from data set Y], or who... [attribute from data set Z].”

Then, using technology – and analytical abilities – they should be proving or disproving those hypotheses so that they are constantly discovering ways to improve the student experience and fulfil the institution’s mission.

Intelligent experiences

Whilst the obstacles to incorporating intelligent data analytics can seem overwhelming, the long-term outcomes outweigh the short-term integration headaches. Transformation through analytics is achievable when all leaders in a higher education institution come together to form one common goal to create what we call ‘intelligent experiences’ for both students and staff.

The challenge is that this type of work is either not any one person’s job at the institution or falls under an area like Institutional Research and Effectiveness. These areas are typically understaffed and charged with handling more basic institutional reporting, compliance and accreditation needs that don’t leave much time for this type of data exploration and analysis.

To help with this, higher education leaders should focus on creating intelligent experiences, which, among other things, can help institutions identify the right combinations of data automatically so this type of exploratory analysis may not even be necessary.

To enable these advances, institutions must spend more time creating organisational cultures that facilitate, recognise and reward efforts to explore and utilise data beyond basic reporting, such as fostering student success, improving teaching and learning or increasing operational efficiency.

Realising change through analytics

At a fundamental level, any institution with an assessment professional is probably doing the types of data analysis that lead to change and improvement since this is the basic premise behind ‘continuous improvement’.

It is important to acknowledge this because there are thousands of examples of institutions doing things like pre- and post-tests around student performance in the classroom or with institutional learning outcomes to recommend changes in curriculum or course-level teaching methodology.

What we’re focused on here, however, are examples of where the data inputs are complex combinations of inter-related variables that, if properly explored, may lead to even greater levels of insight around opportunities to improve.

For example, optimising one’s career trajectory has traditionally required a student to sift through numerous and often non-personalised career development resources. This is followed by a meeting with an advisor to discuss which electives would best complement a given major to provide the student with the most appropriate career skills.

Institutions can simplify this process by aggregating labour market data and correlating it with detailed analyses of academic offerings so that students have a registration-ready schedule aligned with their career aspirations.

In addition, working with institutions to use more data science-driven forms of analysis, such as natural language processing, can help create personalised recommendations for students to engage with outside-the-classroom opportunities that would best expand their career skill exposure.

Higher education leaders must make incremental changes to truly get the best out of data analytics in years to come. Colleges and universities need to form vital analytics functions, a culture of data-driven decision-making and a focus on delivering measurable outcomes.

Doing so will create significant value for their students and teaching staff and ultimately bring sustainability to the future.

Chris Husser is a data evangelist interested in improving teaching and learning, engagement and assessment for higher education and is the vice-president of product management at Anthology.