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Using Machine Learning for Course Success Optimization

CSU San Bernardino is committed to the GI 2025 initiative, and to make the campus efforts more effective, they needed actionable data able to clarify short-term goals, which in turn should improve the long-term outcomes. The Office of Institutional Research (IR) believed that providing timely, accurate, and just-in-time actionable data to the stakeholders that most need it was imperative for supporting students’ course success. IR planned to use a state-of-the-art analytical approach, machine learning, in two ways:

  1. to identify models that explain course failure patterns using historical data and use the models to predict the likelihood of students at risk in key courses such as lower division general education and bottleneck courses
  2. to optimize course scheduling for creating effective pathways to graduation

By identifying students who would benefit the most from interventions prior to the first day of class, IR worked with various offices and programs on campus in launching early intervention strategies to support those students at risk. The Office of Undergraduate Studies worked closely with IR in the identification of bottleneck courses and assessment of their academic support services. The new models will help expand the collaborative efforts to include actionable data for their early interventions strategies and effective schedule planning that accelerates student graduation. This initiative will not only provide quality intervention for students at risk, but also optimize students’ course scheduling and with academic departments to establish clearer degree pathways.

Principal Investigator

Mihaela Popescu, Cal State San Bernardino

Grant Cycle

Spring 2018

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