Students At Risk Project
UX Research Assistant
Apr 2020 — Present
Visual Design- Predictive Modeling
Project Overview
Studies have shown that 33% of students were in good academic standing with a GPA between 2.0 and 3.0 at the end of their first year. This entire population of mid-range GPA students are referred to as “The Murky Middle” where graduation outcomes are difficult to predict.
Of this population, a vast majority(40%) of these students will be late stage dropouts and will not make it to graduation. These Students are frequently underserved because it is difficult to identify this group.

Emerging research from EAB Student Success Collaborative* suggests that rigorous analyses of academic data can separate that hidden population of struggling students from likely graduates. Identification of these students for academic advisors and student support specialists can enable targeted intervention efforts and ultimately improve student retention.

Partnered with IBM the project team has explored the application of predictive analytics models to identify Murky Middle students. The models were developed with the use of IBM Watson Studio / SPSS / machine learning algorithms AND Algonquin College’s Student Information System databases to;

1. Validate “Murky Middle” theory in Algonquin College through 10 years of past student data.

2. Analyze, predict and identify current student intakes elevated risk Murky Middle students, who are at risk of not graduating.

3. Design to identify the “Murky Middle” at the end of their first academic year.

4. Inform Academic Support staff for early assistance and intervention.
Improve the prospects of successful graduation while improving overall retention performance for the college
My Contributions
Lead the design of responsive prototypes for the Students at Risk projects mobile app. Using our partners' applications (IBM) I participated in the modelling of predictive algorithms while designing presentations and print materials.