An increasing number of universities are using predictive analytics to accelerate learning and enhance student success rates.
FREMONT, CA: In the higher education industry, predictive analytics is utilized to forecast future trends that affect student performance and success rates. It employs a mix of big data, algorithms, and machine learning methods.
Machine learning is being used by higher education institutions to fully understand their students regarding how they engage with program content and the overall university experience.
The educational institution will gain the knowledge to construct better systems that give students more personalized and timely support by developing a deeper understanding of what influences success.
Predictive analytics can help institutions to gain a better understanding of the elements that prohibit learners from succeeding. Prominent subject matter experts are already discussing predictive analytics as a way to improve learning, increase engagement, and ensure success.
Predictive analytics for student success
Working with predictive analytics can be difficult, so users must always keep these three recommended practices in mind before delving in:
Give students control over their data
When it comes to data collection, it's critical to give students the opportunity of opting in or out. It is eventually up to the individual to agree. Some institutions look at data to see how students use on-campus and online resources. They analyze class-wide data rather than using personally identifiable information to enhance services.
It is only applicable to the learners who have knowingly 'opted in.'
Restrict access to student data
It's highly unlikely that several employees will need access to all the student information. To ensure that the proper individuals view the correct information, institutions must implement data governance processes.
For example, one employee could only need access to attendance information, while another would have to see how people use the library. Finally, predictive analytics can assist HEIs in shifting from an institutional to a student-centric attitude.
Avoid implicit bias in algorithms
Algorithms could aggravate the effect of systemic prejudice if institutions aren't careful. After all, it was people who created the algorithms in the first place.
If an HEI uses identifiers like postcode, secondary school, or ethnicity in their algorithms, they risk underserving students' requirements.