Supporting Teachers in Responsive Instruction for Developing Expertise in Science (Collaborative Research: Riordan)

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With the increasing use of online interactive environments for science and engineering education in grades K-12, there is a growing need for detailed automatic analysis of student explanations to provide targeted and individualized guidance. In this work we describe a process of human annotation of student ideas based on deconstructed holistic scoring rubrics for knowledge integration in science learning and develop new NLP methods for identifying diverse expressions of student ideas and reasoning.

Co-PI(s): Marcia Linn and Libby Gerard, University of California-Berkeley

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