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Using Natural Language Processing Tools to Classify Student Responses to Open-Ended Engineering Problems in Large Classes
Author(s) -
Matthew Verleger
Publication year - 2020
Publication title -
papers on engineering education repository (american society for engineering education)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--23271
Subject(s) - rubric , computer science , context (archaeology) , sample (material) , set (abstract data type) , quality (philosophy) , decision tree , natural language processing , machine learning , random forest , artificial intelligence , natural language , matching (statistics) , data mining , data science , mathematics education , statistics , paleontology , philosophy , chemistry , mathematics , epistemology , chromatography , biology , programming language
Matthew Verleger is Assistant Professor in Freshman Engineering at Embry-Riddle Aeronautical University. He has a BS in Computer Engineering, an MS in Agricultural & Biological Engineering, and a PhD in Engineering Education, all from Purdue University. Prior to joining the Embry-Riddle faculty, he spent two years as an Assistant Professor of Engineering Education at Utah State University. His research interests include Model-Eliciting Activities, online learning, and the development of software tools to facilitate student learning.

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