http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Artificial intelligence in STEM education
the paradigmatic shifts in research, education, and technology
1 online resource (xi, 383 pages) : illustrations
"Artificial intelligence (AI) opens new opportunities for science, technology, engineering and mathematics (STEM) education. AI in education (AIED) has undergone several research, practice, and technological paradigmatic shifts in its brief history, which can be particularly observed in STEM education. The development of AI in STEM education has experienced significant shifting of the focuses during the last decade. In general, it has been reshaped to gradually emphasize the learner-centered, while weakening the teacher-directed instruction mode. This has led to a change between the three paradigms: leaner-receiver, learner-partner, learner-leader. Consequently, supporting AI frameworks have been significantly improved from intelligent tutoring systems (ITSs), exploratory learning environments (ELEs) to human-centered participatory (HCP) smart education. This book aims to track the paradigmatic shifts of AI in STEM education and intends to highlight the reasons behind these changes as well as explain how the AI techniques have been improved to ensure the efficiency of the shifts. AI in STEM Education is a must-read for researchers, engineers, educators and policy-makers who are interested in opportunities and challenges of this growing field"--
Artificial Intelligence in STEM education : current developments and future considerations / Fan Ouyang, Pengcheng Jiao, Amir H. Alavi, and Bruce M. McLaren -- Towards a deeper understanding of K-12 students' CT and engineering design processes / Gautam Biswas and Nicole M. Hutchins -- Intelligent science stations bring AI tutoring into the physical world / Nesra Yannier, Scott E. Hudson, and Kenneth R. Koedinger -- Adaptive support for representational competencies during technology-based problem-solving in STEM / Martina A. Rau -- Teaching STEM subjects in non-STEM degrees : an adaptive learning model for teaching statistics / Daniela Pacella, Rosa Fabbricatore, Alfonso Iodice D'Enza, Carla Galluccio, and Francesco Palumbo -- Removing barriers in self-paced online learning through designing intelligent learning dashboards / Arta Faramand, Hongxin Yan, M. Ali Akber Dewan, and Fuhua Lin -- PASTEL : evidence-based learning engineering methods to facilitate creation of adaptive online courseware / Noboru Matsuda, Machi Shimmei, Prithviraj Chaudhuri, Dheeraj Makam, Raj Shrivastava, Jesse Wood, and Peeyush Taneja -- A technology-enhanced approach for locating timely and relevant news articles for context-based science education / Jinnie Shin and Mark J. Gierl -- Adaptive learning profiles in the education domain / Claudio Giovanni Demartini, Andrea Bosso, Giacomo Ciccarelli, Lorenzo Benussi, and Flavio Renga -- Teacher orchestration systems supported by AI : theoretical possibilities and practical considerations / Suraj Uttamchandani, Haesol Bae, Chen Feng, Krista Glazewski, Cindy E. Hmelo-Silver, Thomas Brush, Bradford Mott, and James Lester -- The role of AI to support teacher learning and practice : a review and future directions / Jennifer L. Chiu, James P. Bywater, and Sarah Lilly -- Learning outcome modeling in computer-based assessments for learning / Fu Chen and Chang Lu -- Designing automated writing evaluation systems for ambitious instruction and classroom integration / Lindsay Clare Matsumura, Elaine L. Wang, Richard Correnti, and Diane Litman -- Promoting STEM education through the use of learning analytics : a paradigm shift / Shan Li and Susanne P. Lajoie -- Using learning analytics to understand students' discourse and behaviors in STEM education / Gaoxia Zhu, Wanli Xing, Vitaliy Popov, Yaoran Li, Charles Xie, and Paul Horwitz -- Understanding the role of AI and learning analytics techniques in addressing task difficulties in STEM education / Sadia Nawaz, Emad A. Alghamdi, Namrata Srivastava, Jason Lodge, and Linda Corrin -- Learning analytics in a Web3D based inquiry learning environment / Guangtao Xu, Yingqian Li, Zhouyang Zhu, Yihui Hu, and Wenting Zhou -- On machine learning methods for propensity score matching and weighting in educational data mining applications / Juanjuan Fan, Joshua Beemer, Xi Yan, and Richard A. Levine -- Situating AI (and big data) in the learning sciences : moving toward large-scale learning sciences / Danielle S. McNamara, Tracy Arner, Reese Butterfuss, Debshila Basu Mallick, Andrew S. Lan, Rod D. Roscoe, Henry L. Roediger III, and Richard G. Baraniuk -- Linking natural language use and science performance / Scott Crossley, Danielle S. McNamara, Jennifer Dalsen, Craig G. Anderson, and Constance Steinkuehler -- Quick Red Fox : an app supporting a new paradigm in qualitative research on AIED for STEM / Stephen Hutt, Ryan S. Baker, Jaclyn Ocumpaugh, Anabil Munshi, J.M.A.L. Andres, Shamya Karumbaiah, Stefan Slater, Gautam Biswas, Luc Paquette, Nigel Bosch, and Martin van Velsen -- A systematic review of AI applications in computer-supported collaborative learning in STEM education / Jingwan Tang, Xiaofei Zhou, Xiaoyu Wan, and Fan Ouyang -- Inclusion and equity as a paradigm shift for Artificial Intelligence in education / Rod D. Roscoe, Shima Salehi, Nia Nixon, Marcelo Worsley, Chris Piech, and Rose Luckin.
edited by Fan Ouyan, Pengcheng Jiao, Bruce M. McLaren and Amir H. Alavi.
Includes bibliographical references and index.
Dr. Fan Ouyang is a research professor in the College of Education at Zhejiang University. Dr. Ouyang holds a Ph.D. degree fromthe University of Minnesota.Her research interests are computer-supported collaborative learning, learning analytics and educational data mining, online and blended learning, and artificial intelligence in education. Dr. Ouyang has authored/coauthored more than 30 SSCI/SCI/EI papers and conference publications and worked as PI/co-PI on more than 10 research projects, supported by National Science Foundation of China (NSFC), Zhejiang Province Educational Reformation Research Project, Zhejiang Province Educational Science Planning and Research Project, Zhejiang University-UCL Strategic Partner Funds, etc. Dr. Pengcheng Jiao is a research professor in the Ocean College at the Zhejiang University, China. His multidisciplinary research integrates structures and materials, sensing, computing, networking, and robotics to create and enhance the smart ocean. His research interests include mechanical functional metamaterials, SHM and energy harvesting, marine soft robotics and AIEd. In recent years, he has authored/co-authored more than 100 peer-reviewed journal and conference publications and worked as PI/co-PI on more than 10 research projects. Dr. Bruce M. McLaren is an Associate Research Professor at Carnegie Mellon University, current Secretary and Treasurer and past President of the International Artificial Intelligence in Education Society (2017-2019). McLaren is passionate about how technology can support education and has dedicated his work and research to projects that explore how students can learn with educational games, intelligent tutoring systems, e-learning principles, and collaborative learning. He holds a Ph.D. and M.S. in Intelligent Systems from the University of Pittsburgh, an M.S. in Computer Science from the University of Pittsburgh, and a B.S. in Computer Science (cum laude) from Millersville University. Dr. Amir H. Alaviis an Assistant Professor in the Department of Civil and Environmental Engineering and Department of Bioengineering at the University of Pittsburgh. He holds a PhD degree in Civil Engineering from Michigan State University. His original and seminal contributions to developing and deploying advanced machine learning and bio-inspired computation techniques have established a road map for their broad applications in various engineering domains. He is among the Web of Science ESI's World Top 1% Scientific Minds in 2018, and the Stanford University list of Top 1% Scientists in the World in 2019 and 2020.
Chapman & Hall/CRC artificial intelligence and robotics series