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This REU will enable young researchers to analyze this data and contribute to understanding high-impact teaching practices, how to group learners for instruction, and what type of feedback or remediation is most applicable to a learner. These research activities will provide new information about the effectiveness of both technological and traditional educational interventions for a variety of learners in secondary school classrooms. Undergraduates will analyze data using educational data mining techniques, such as student modeling to better estimate intervention impacts and clustering to determine which students are most similar. Participating students will enjoy in person, larger group activities with the REU site hosted by Dr. Rundensteiner in Data Science.