Predictive Models

Identifying incoming students who are at risk of struggling academically is critical for higher education institutions to provide timely and effective support.   This process enables higher education institutions to provide targeted support and resources to students who need it the most, which in turn increases the chances of their success. 

 Proactively identifying and assisting students at risk of academic struggles mid-semester is crucial for higher education institutions. By leveraging early alerts, mid-term grades, and comprehensive student engagement data, institutions can swiftly pinpoint challenges and provide targeted support.  

Early warning signals, coupled with mid-term grades, offer a nuanced understanding of academic progress, enabling timely interventions. A holistic evaluation of student engagement across campus activities provides insights into overall well-being.  Over multiple years, one institution saw the average second-year retention of those identified as needing high-support throughout the semester was close to 50%.

Higher education institutions often face the challenge of students struggling and dropping out in certain courses. This can lead to lower retention rates and a less skilled workforce. By identifying courses or combinations of courses that are difficult for students, institutions can provide support and resources to help them succeed.  This can be particularly important for institutions that create incoming student schedules.