INTO, a renowned international education services provider, has announced the launch of a state-of-the-art machine learning model designed to predict and mitigate student melt in university admissions.
This AI-powered tool forms part of INTO’s broader initiative to harness artificial intelligence throughout the student admissions journey, improving recruitment, retention, and institutional success.
Student melt—where students withdraw after confirming enrolment—presents a major challenge for universities. INTO’s new machine learning system delivers precise forecasts and practical insights, helping institutions reduce melt rates and enhance efficiency.
With its AI-driven admissions processing, INTO has already slashed application handling times from weeks to mere hours. This latest innovation further cements its position at the forefront of AI-powered education services.
“This new machine learning model represents a significant leap forward for the higher education sector in managing student enrollment,” said Andy Fawcett, INTO’s Chief Technology Officer and Executive Vice President of Global Admissions.
“With precise forecasts and actionable insights, we are equipping universities with the tools they need to navigate the complexities of student retention and enhance their financial performance.
“By analysing a vast array of data points, the system delivers precise predictions and enables institutions to proactively address student needs. This proactive approach helps universities optimise their resources and strategies, ensuring a more efficient and effective enrollment process.”
- Advanced precision forecasting: The model uses sophisticated algorithms to categorize students into various risk bands, ranging from “rare chance” to “almost certain” to melt. By analyzing over 70 different data points, including unique factors such as student visa status and visa preparedness, the model delivers precise forecasts that enable institutions to plan more strategically.
- Granular data analysis: The model allows institutions to drill down into individual student data and specific institutional patterns, offering actionable insights to identify high-risk areas and allocate resources where they are most needed.
- Real-time updates and validation: The system is updated daily with live data, providing the most current predictions and validating them against actual outcomes, ensuring accuracy and enabling continuous refinement.
- Actionable insights for effective interventions: Beyond forecasting, the model identifies students at risk of melt and provides strategies for personalized interventions such as outreach or visa support, enabling institutions to address issues proactively.
