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© 2020

Artificial Intelligence Supported Educational Technologies

  • Niels Pinkwart
  • Sannyuya Liu
Book

Part of the Advances in Analytics for Learning and Teaching book series (AALT)

Table of contents

  1. Front Matter
    Pages i-x
  2. Overviews

    1. Front Matter
      Pages 1-1
    2. Arham Muslim, Mohamed Amine Chatti, Mouadh Guesmi
      Pages 3-29
    3. Fang Haiguang, Wang Shichong, Xue Shushu, Wang Xianli
      Pages 45-58
  3. Systems

    1. Front Matter
      Pages 75-75
    2. Zhou Long, Frank Andrasik, Kai Liu, Xiangen Hu
      Pages 77-91
    3. Albrecht Fortenbacher, Haeseon Yun
      Pages 93-114
    4. Tai Wang, Yu-chen Liu, Zhi Liu, Ming Zhang, Jiao Liu, Ya-mei Zhu
      Pages 115-132
    5. Qing Li, Yuan Ren, Tianyu Wei, Chengcheng Wang, Zhi Liu, Jieyu Yue
      Pages 133-150
  4. Algorithms

    1. Front Matter
      Pages 151-151
    2. Jingying Chen, Guangshuai Wang, Kun Zhang, Ruyi Xu, Dan Chen, Xiaoli Li
      Pages 153-174
    3. Liang Zhao, Kun Chen, Zhi Liu, Jie Song, Xiaoliang Zhu, Ming Xiao et al.
      Pages 193-207
    4. Siyu Yao, Ruijie Wang, Shen Sun, Derui Bu, Jun Liu
      Pages 209-224
  5. Empirical Studies

About this book

Introduction

This book includes a collection of expanded papers from the 2019 Sino-German Symposium on AI-supported educational technologies, which was held in Wuhan, China, March, 2019. The contributors are distinguished researchers from computer science and learning science.

 

The contributions are organized in four sections: (1) Overviews and systematic perspectives , (2) Example Systems, (3) Algorithms, and (4) Insights gained from empirical studies. For example, different data mining and machine learning methods to quantify different profiles of a learner in different learning situations (including interaction patterns, cognitive modes, knowledge skills, interests and emotions etc.) as well as connections to measurements in psychology and learning sciences are discussed in the chapters.

 

  • Provides cross-cultural perspectives on artificial intelligence applications in education
  • Offers contributions by distinguished researchers from computer science and learning science
  • Provides an broad view on both systems/algorithms and educational aspects of current challenges in AI-supported educational technologies

Keywords

Artificial intelligence Learning analytics Educational technology Study of game-based learning Semantic similarity

Editors and affiliations

  • Niels Pinkwart
    • 1
  • Sannyuya Liu
    • 2
  1. 1.Institut für InformatikHumboldt-Univ. zu BerlinBerlinGermany
  2. 2.National Engineering Research Center for E-learningCentral China Normal UniversityWuhanChina

About the editors

Niels Pinkwart has a background in Computer Science and Mathematics. He received his PhD from the University of Duisburg-Essen in 2005 with a dissertation on collaborative educational modeling systems. After a post-doctoral position at the Human-Computer Interaction Institute of Carnegie Mellon University, he accepted offers for Assistant Professor and Associate Professor positions at Clausthal University of Technology. In 2013, he moved to the Humboldt-Universität zu Berlin where he since heads the research group "Computer Science Education / Computer Science and Society", the ProMINT Kolleg and the Center of Technology Enhanced Learning located in the Professional School of Education of HU Berlin. In addition to his activities at HU Berlin, Prof. Pinkwart acts as Principal Investigator at the Einstein Center Digital Future and at the Weizenbaum Institute for the networked society (German Internet Institute), and holds a scientific director position at DFKI (German Research Center for Artificial Intelligence) since 2019. Within the German Computer Science Association, Prof. Pinkwart is currently co-chair of the working group on Learning Analytics and member of the steering committee of the section on Educational Technologies.

Sannyuya Liu has been a professor and the executive deputy director in National Engineering Research Center for E-learning (NERCEL), Central China Normal University (CCNU) since 2006.  He received the Ph.D. degree in systems engineering from Huazhong University of Science and Technology (HUST), Wuhan, China, in 2003.  His research interests include educational data mining, AI in education, ICT in education, educational technology, etc.  To date, he has published more than 100 peer-reviewed articles, 7 books, and more than 30 national invention patents.  He has also led many National Key R&D Projects of China on Personalized Learning and Intelligent Tutoring Technologies, e.g. he has been funded by National Natural Science Foundation of China on Group Behavior-Emotion-Cognition Modeling in Online Learning.  He successfully led NERCEL R&D team in developing starC, a SPOC learning platform based dual-track teaching philosophy for supporting Informatization reform and teaching innovation in higher education.  He was awarded with one First and one Secondary Prizes for Scientific and Technological Progress of Hubei Province respectively.  Particularly, he proposed a systematic framework of Quantified Learning in a book named “Quantified Learning: Data-Driven Learning Behavior Analysis” published by Science Press of China, which has a significant impact on making the data-driven human learning mechanism research more accessible and promoting more applications of big data in education.

 

Bibliographic information

  • Book Title Artificial Intelligence Supported Educational Technologies
  • Editors Niels Pinkwart
    Sannyuya Liu
  • Series Title Advances in Analytics for Learning and Teaching
  • Series Abbreviated Title Advances in Analytics for Learning and teach.
  • DOI https://doi.org/10.1007/978-3-030-41099-5
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Education Education (R0)
  • Hardcover ISBN 978-3-030-41098-8
  • Softcover ISBN 978-3-030-41101-5
  • eBook ISBN 978-3-030-41099-5
  • Series ISSN 2662-2122
  • Series E-ISSN 2662-2130
  • Edition Number 1
  • Number of Pages X, 297
  • Number of Illustrations 25 b/w illustrations, 46 illustrations in colour
  • Topics Educational Technology
    Computers and Education
    Artificial Intelligence
  • Buy this book on publisher's site