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Overcoming data privacy and ethics concerns to uplift student experiences and outcomes

27 April, 2022 | by Sacha Nouwens, Executive Director, Student Experience and Insights

Learning analytics – using data to tailor experiences for existing students – is an area of huge potential and interest for universities. But while it is widely understood that learning analytics can optimise student experiences and study outcomes, many institutions are yet to move forward.

A big hurdle relates to uncertainty around data privacy and ethics. Yet research reveals that tertiary students support the collection of their learning data so long as they understand how it will be used. Moreover, in today’s competitive higher education landscape, failing to leverage the power of learning analytics poses a real and present commercial risk.

With these factors in mind, and having worked with numerous university partners to implement the successful and ethical use of learning analytics, I would like to share what I’ve learned.

1. Now is the time to leverage learning analytics

There is an overwhelming case for using data to optimise higher education learning and teaching. When effectively applied, learning analytics can significantly uplift course design and quality, guide institutional decision-making and support students to complete their study goals. Learning analytics driven initiatives are used at universities around the world, with many achieving impressive student success outcomes.

When institutions consider the benefits to the individual student, and to the institution, failing to act on learning analytics could leave universities trailing behind other private education providers. There is also a commercial risk attached to inaction, as retention, student experience ratings and graduate outcomes draw increasing focus in a competitive landscape.

2. Data privacy and ethics must be balanced against student experience and service expectations

Today’s learners increasingly expect personalised digital experiences. They are used to accepting website cookies and receiving personalised content suggestions on social media, they expect targeted digital advertising and value the time savings afforded by predictive services like Google’s search autocomplete.

Yet universities understandably set higher standards for themselves than many commercial organisations – in particular the world’s tech giants. And although they have experienced a lifetime of personal data collection and use, research tells us that today’s tertiary students do care about data privacy and ethics. A recent Finnish study examining student perceptions about data safety and ethics in learning analytics suggests students are broadly supportive of the learning analytics concept as a strategy for their institution to help them achieve their study goals, however they have a limited understanding of how their data is being collected and used. Similarly, a recent report looking at the attitudes of American college students revealed that they “care deeply about data privacy, and…they want universities to use their personal information predominantly for educational purposes.” The report also revealed that “college students often do not know how their institutions use their data.”

More broadly, according to the 2020 Australian Attitudes to Privacy survey, Australians are increasingly questioning data practices where the purpose for collecting personal information is unclear. Australians also object to the use of their data for “unwanted marketing communications [and to] information being collected when it was not required.”

While universities don’t send unsolicited marketing communication or on-sell data they collect for learning analytics, there is a clear opportunity to empower students (and teaching staff) with information about the use and role of data. At OES, we recommend our university partners are transparent by providing clear and timely information about what data is being collected and how it is being used to enhance learning, teaching and the overall study experience. That disclosure needs to be accessible and ever-present.

3. Inform teaching staff to promote understanding and buy-in

From a teaching perspective, university staff sometimes flag their concern that learning analytics insights could generate unwanted bias. Understandably, academics don’t want to see learning analytics deliver ‘self-fulfilling prophecies’ for students whose demographics or personal circumstances predispose them to a higher rate of failure. Staff are also concerned about the emotional bias created by knowing predicted student outcomes through advanced learning analytics.

In fact, the best practice application of learning analytics assumes that every tertiary student has an equal chance of success. At least, that’s our assumption at OES. Demographic indicators are typically unimportant in the learning analytics projects we deliver for university partners. Most important in our data-led insights are the actions students take as they learn – either individually or collectively – and what we can glean from these actions to better support those students.

4. Leverage existing policies while developing your own

Finally, I often hear about the challenges universities face to develop and roll out learning analytics privacy and ethics policies. While creating such policies is absolutely vital, stagnating at this mark presents an unacceptable delay and is a risky approach for 21st century institutions to take. Universities at the start of their learning analytics journey can leverage the considerable body of work that already exists, leaning on established international policies while proactively developing their own. Of course, these policies and the way they are communicated should not be fixed. As with every policy, universities will need to continually refine their data practices as they scale up their learning analytics capabilities.

With all this in mind, universities have an opportunity and an imperative to simply make a start. A single, discrete project can serve as a springboard for further exploration and application. For example, using collective insights about student performance, engagement and even qualitative feedback to review the design of a single unit. Even a small learning analytics project can have a tangible impact to improve programs, help students keep up their studies and inform university-wide decision-making.

By understanding student data expectations, communicating clearly, leveraging existing policies and procedures and carefully dipping a toe into the metaphorical data lake, even the most cautious universities can start using learning analytics to support their students to succeed.