rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Artificial intelligence in STEM education
    sub title: the paradigmatic shifts in research, education, and technology
  name:
    name part: Ouyan, Fan
    role:
      role term: editor.
  name:
    name part: Jiao, Pengcheng
    role:
      role term: editor.
  name:
    name part: McLaren, Bruce M.
    role:
      role term: editor.
  name:
    name part: Alavi, Amir H.
    role:
      role term: editor.
  name:
    name part: Taylor & Francis
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: flu
    date issued: 2023
    copyright date: 2023
    edition: First edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xi, 383 pages) : illustrations
  abstract: "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"--
  table of contents: 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 in…
  note: edited by Fan Ouyan, Pengcheng Jiao, Bruce M. McLaren and Amir H. Alavi.
  note: Includes bibliographical references and index.
  note: 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 …
  subject:
    topic: Artificial intelligence
    topic: Educational applications
  subject:
    topic: Science
    topic: Study and teaching
    topic: Technological innovations
  subject:
    topic: Intelligence artificielle
    topic: Applications en éducation
  subject:
    topic: Sciences
    topic: Étude et enseignement
    topic: Innovations
  subject:
    topic: EDUCATION / Higher
  subject:
    topic: TECHNOLOGY / General
  subject:
    topic: Artificial intelligence
    topic: Educational applications
  subject:
    topic: Science
    topic: Study and teaching
    topic: Technological innovations
  classification: LB1028.43 .A795 2023
  classification: 371.33/4
  related item:
    title info:
      title: Artificial intelligence in STEM education
    origin info:
      publisher: Boca Raton, FL : CRC Press, 2023
      edition: First edition.
    identifier: (DLC) 2022028544
  related item:
    title info:
      title: Chapman & Hall/CRC artificial intelligence and robotics series
  identifier: 9781000814712
  identifier: 1000814718
  identifier: 9781003181187
  identifier: 100318118X
  identifier: 9781000814750
  identifier: 1000814750
  identifier: 9781032009216
  identifier: 1032009217
  identifier: 9781032019604
  identifier: 2022028545
  identifier: 9781003181187 Taylor & Francis
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: DLC
    record creation date: 220906
    record change date: 20241108165737.0
    record identifier: in00005523018
    language of cataloging:
      language term: eng