INTO has launched a new machine-learning model designed to tackle the critical challenge of attrition in international student enrolment.
The new tool is part of INTO’s broader strategy to leverage AI across the admissions lifecycle, from recruitment to retention, enhancing institutional performance and student outcomes.
Enrolment melt, or more commonly referred to as attrition in Australia, is where students withdraw their commitment after confirming. Students who drop out along the journey prior to starting has substantial consequences for educational institutions. INTO’s new machine learning model, as stated by INTO, addresses this issue with great accuracy and actionable insights, enabling institutions to better manage and reduce melt/attrition rates.
INTO’s recent advancements in AI-enabled admissions processing have already cut processing times from weeks to just hours, and this latest innovation further strengthens its AI-driven service portfolio.
Andy Fawcett, INTO’s Chief Technology Officer and Executive Vice President of Global Admissions said, “This new machine learning model represents a significant leap forward for the higher education sector in managing student enrolment.”
“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 analyzing a vast array of data points, the system delivers precise predictions and enables institutions to proactively address student needs. This proactive approach helps universities optimize their resources and strategies, ensuring a more efficient and effective enrollment process.”
In a statement received by the Koala the key features and benefits of INTO’s AI-driven Machine Learning Model include:
- 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 analysing 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.
Currently, the model is being utilized by key teams within INTO University Partnerships, including the enrolment forecasting team. The transition to Microsoft Fabric, an advanced analytical platform, will further enhance the model’s capabilities, streamlining data processing and expanding its functionality.
“By integrating AI tools, INTO is streamlining not only recruitment but the entire admissions process for its partner universities, enhancing their ability to respond quickly and effectively to both student and institutional needs,” said Fawcett.
The Koala wishes INTO well with its ongoing endeavour of integrating AI into the student journey, which benefits students and institutions alike.
