TRU-Lift
TRU-Lift is a predictive analytics platform designed to identify students who may benefit from early academic interventions. By analyzing learning behavior patterns and engagement metrics, the system enables proactive support rather than reactive responses.
Key Features
- Early Warning Indicators: Machine learning models that predict academic risk factors
- Actionable Insights: Clear recommendations for instructors and advisors
- Privacy-Preserving: Ethical use of student data with transparent algorithms
- Intervention Tracking: Monitor the effectiveness of support strategies over time
Impact
TRU-Lift moves beyond descriptive analytics to predictive and prescriptive approaches, helping institutions support student success through timely, targeted interventions.
I am a tenure-track Assistant Professor in the Computer Science department at Thompson Rivers University. My research centers on the impact of generative AI on the learning behavior and outcome in computer science education. Before joining TRU, I was a Postdoctoral Fellow at the UBC Master of Data Science, where I developed and taught a variety of data science courses, including those on statistical inference, machine learning, and technical communication. In addition to teaching, I coordinated the capstone program, facilitating student collaborations with industry partners on real-world data science projects.
Prior to UBC, I worked as a Postdoctoral Fellow in Learning Analytics at the School of Information, University of Michigan. My research focuses on analyzing students social interactions and peer effects from spatio-temporal large scale data. My work has been recognized with competitive grants, and multiple best paper awards at prominent conferences, including LAK18 and HCI International 17.
I hold a PhD in Learning Analytics at The Open University UK, a BSc and MSc in Economics from Maastricht University, Netherlands.