Although the technology is in its infancy, forward-thinking universities are already reaping the rewards of predictive success.
A growing number are using it to do the following:
- Adapt and personalise the learning experience to make it more efficient and effective.
- Identify individuals who are at risk of dropping out by tracking patterns of behaviour, and using these insights to intervene early.
- Accelerate students’ learning by moving quickly through the content they know and providing enhanced support in areas they are yet to master.
- Monitor student engagement and performance in real-time to identity macro trends.
- Gain a better understanding of student comprehension in order to inform assignment, content and programme design.
- Improving the ability to reach the most vulnerable students without hiring more staff.
- Re-allocate time to impactful work such as having transformational conversations with students, and programme mapping.
Some higher education professionals are concerned that monitoring students to such a degree constitutes an invasion of privacy.
Critics also warn that algorithms could in fact reinforce historical inequalities.
So, how can you use predictive success responsibly?
Predictive for student success: 3 best practices
Working with predictive can be challenging, so you should always consider these 3 best practices before diving deep into this effort:
1: Avoid implicit bias in algorithms
If institutions aren’t careful, algorithms could in fact exacerbate the impact of structural bias. After all, human beings are the ones programming the algorithms in the first place.
2: Give students control over their data
When it comes to gathering data, it’s important to provide students with the option to opt in or out. Ultimately, it’s up to the individual to give consent.
With this in mind, how can you encourage students to opt in?
- Be transparent about how data is used and maintained
- Deliver personalised and tailored content
- Ensure understand exactly what they will be missing out on if they don’t opt in
- Investing in smart, AI-based solutions to automate unsubscribes
Some universities analyse data to find out how students are utilising on-campus or online services. Instead of using personally identifiable information, they monitor class-wide data in order to improve services.
This only applies to the students who have actively ‘opted in’ though.
3: Restrict access to student data
It’s unlikely that many staff members will require access to the full breadth of student data. Institutions need to have data governance practices in place to ensure that the right people see the right information.
For example, one employee may only require access to attendance statistics whereas another may also need to see metrics on how individuals are using the library service.
Ultimately, predictive can help, like the one below, move from an institutional mindset to a student-centric one.
