How Predictive Models Can Help Struggling STEM Students Stay on Track #STEMSuccess #studentsupport

How Predictive Models Can Help Struggling STEM Students Stay on Track
Plumley, R. D., Bernacki, M. L., Greene, J. A., Kuhlmann, S., Raković, M., Urban, C. J., ... & Gates, K. M. (2024). Co‐designing enduring learning analytics prediction and support tools in undergraduate biology courses. British Journal of Educational Technology.
Even the most motivated students can lose their way in challenging STEM courses. To tackle this, researchers worked with subject experts to redesign courses and track student engagement through digital data. By analyzing just two weeks of classroom interactions, they developed models that accurately identified struggling students, allowing for timely intervention.
These data-driven insights not only helped support students but also improved future course designs. This research highlights the power of learning analytics to keep students on track and enhance learning outcomes.
#STEMSuccess #StudentSupport #DataDrivenLearning #PredictiveAnalytics #EducationTechnology