Abstract:
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Constructed-response questions, those in which students must respond to a posed question using their own words, have been shown to help researchers and instructors understand students' knowledge and understanding better than multiple choice questions. One principle advantage of these open-ended questions is that students are able to elaborate, often both correctly and incorrectly, about how they arrived at their answers. One effective approach is to leverage machine learning algorithms to make classifications about student responses. In this presentation, I will utilize several machine learning algorithms together in an ensemble to classify student constructed responses and provide real-time instructor feedback.
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