Student Engagement Engine

In the era of intelligent transformation in higher education, Curtin University has developed an innovative data driven solution to enhance and support student success. The Student Engagement Engine represents a significant step forward in utilising machine learning models to identify and support at-risk students while reducing manual processes and overheads.

The project involved collaboration with Digital Technology Solutions (Curtin IT team), Amazon Web Services and external data scientists to create a sophisticated machine learning model using AWS environments and expertise. This MVP model analysed over 130 features extracted from key data sources, including the Student Management System, Customer Relationship Management System, and Learning Management System. Through careful feature selection, we identified the most significant predictors of student performance, striking a balance between model complexity and runtime efficiency.

To translate complex predictive analytics into actionable insights, we developed a fresh approach to data visualisation. The model's outputs are then synthesized into key business dimensions 
The model’s output, through the core CRM facilitates automated communications and enables proactive outreach to students enabling the Support teams to make timely data-driven interventions and decisions while providing opportunities to personalise support to students.  

Through combining the power of machine learning and data analytics, we've embarked on a journey to create timely personalised student support.  By exploring our journey from data ingestion to automated interventions, and the adoption of an artificial intelligence data assistant (AIDA), participants will discover practical strategies for leveraging AI and data analytics to enhance support services, improve learner retention rates, and drive institutional success.