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Argument Mining from Text for Teaching and Assessing Writing

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The written and diagrammed arguments of students (and the mappings between them) are educational data that can be automatically mined for purposes of student instruction and assessment. This talk will illustrate some of the opportunities and challenges in educationally-oriented argument mining from text. I will first describe how we are using natural processing to develop argument mining systems that are being embedded in two types of educational technologies: computerized essay grading and computer-supported peer review. I will then present the results of empirical evaluations of these technologies, using argumentative writing data obtained from elementary, high school, and university students.

Bio: Diane Litman is Professor of Computer Science, Senior Scientist with the Learning Research and Development Center, and Co-Director of the Graduate Program in Intelligent Systems, all at the University of Pittsburgh. She is also a former Chair of the North American Chapter of the Association for Computational Linguistics. Dr. Litman’s current research focuses on enhancing the effectiveness of educational technology through the use of spoken and natural language processing (e.g., spoken tutorial dialogue systems, text summarization for classroom apps, and argument mining).

This talk is part of the NLIP Seminar Series series.

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