We explored how contextual learner data—like sensory load and time constraints—could power smarter content, outreach, and system-level decisions. Built on our peer-reviewed GEM framework, the work reframed tracking from behavior to barriers.
We planned a solution that enabled support staff to view student mindsets and tailored recommendations to guide their experience both overtly and through automated views.
Our existing systems primarily tracked behavioral data—logins, clicks, submissions—which didn't capture the nuanced challenges students face. This gap meant that many contextual factors influencing student engagement remained invisible, limiting our ability to provide timely and effective support.
My objective was to integrate GEM into our data infrastructure to capture these overlooked contextual factors. This involved collaborating with a vendor-led faculty dashboard project and exploring new avenues to develop a system that could inform both individual interventions and broader design decisions.
I partnered with cross-functional teams to identify key GEM factors suitable for tracking. We worked with the vendor to map these factors into their existing dashboard framework and simultaneously developed prototypes for a new system capable of generating real-time, personalized recommendations based on contextual data.
The first test of the data gathering process was through Pendo Guides and a low-momentum segement of the user population.
The pilot tools demonstrated the potential of integrating contextual data into decision-making processes. Faculty gained clearer insights into student challenges, enabling more targeted support, while the groundwork was laid for a system that could adapt learning experiences in real-time based on individual student contexts.
The staff-facing dashboard enabled filtering by school, program code, time periods, student name, and student ID. Staff could receive generated own guidance or write their own, take notes on student interactions, prompt the student through platform events or contact them directly.
A sizeable portion of the staff preferred the darker variant palette for their dashboard experience.
