Track 4. Digital Game and Intelligent Toy Enhanced Learning (DIGITEL@ICALT2016)

Track Chairs

Chang Maiga Chang, Athabasca University, Canada [Coordinator -]
diaz Paloma Diaz, Universidad Carlos III de Madrid, Spain
Fernandez Baltasar Fernández-Manjón, Complutense University of Madrid, Spain
shih Ju-Ling Shih, National University of Tainan, Taiwan
Wong Seng Yue, Taylor's University, Malaysia

Track Program Committee

  • Ah-Fur Lai, University of Taipei, Taiwan
  • Barry Lee Reynolds, National Yang-Ming University, Taiwan
  • Chuen-Tsai Sun, National Chiao Tung University, Taiwan
  • Chun-Yi Shen, Tamkang University, Taiwan
  • Fong-Lok Lee, Chinese University of Hong Kong, Hong Kong
  • Hao-Chiang Koong Lin, National University of Tainan, Taiwan
  • Hercy Cheng, Central China Normal University, China
  • Hiroyuki Mitsuhara, Tokushima University, Japan
  • ing-Puu Chen, National Taiwan Normal University, Taiwan
  • Jianhua Wu, Central China Normal University, China
  • Jie-Chi Yang, National Central University, Taiwan
  • Junjie Shang, Peking University, China
  • Liz Alison Bacon, University of Greenwich Old Royal Naval College, UK
  • M. Ali Akber Dewan, Athabasca University, Canada
  • Michal Ptaszynski, Kitami Institute of Technology, Japan
  • Ming-Fong Jan, National Central University, Taiwan
  • Minghsin Tsai, Asia University, Taiwan
  • Nian-Shing Chen, National Sun Yat-sen University, Taiwan
  • Robert Heller, Athabasca University, Canada
  • Roberto Sebastian Legaspi, The Institute of Statistical Mathematics, Japan
  • Sheng-Kai Yin, Mingdao University, Taiwan
  • Shu-Yuan Tao, Takming University of Science and Technology, Taiwan
  • Tak-Wai Chan, National Central University, Taiwan
  • Tsai-Yen Li, National Chengchi University, Taiwan
  • Tsung-Yen Chuang, National University of Tainan, Taiwan
  • Wolfgang Müller, University of Education Weingarten, Germany
  • Yen-Hung Hu, Nolfolk State University, USA
  • Yu-Ren Yen, Far East University, Taiwan
  • Zhi-Hong Chen, Yuan-Ze University, Taiwan

Track Description and Topics of Interest

Learning can take place anytime and anywhere, in particular in the context of lifelong and informal learning. Learners have their own learning styles. Some may prefer reading alone over listening to a lecture and some may want to get their hands dirty in hands-on practices. While some may be impressed and can learn quickly through watching a documentary movie, some others may want to interact with peers either physically or virtually (sometimes via avatars or even with virtual characters). No matter what preference learners have, engagement and motivations are the key ingredients for effective and efficient learning. Toy, robot and game technologies attract learners’ attention, engaging them through highly interactive and immersive experiences. Such technologies also satisfy the various learning needs as well as to assessing learners' skills knowledge unobtrusively and implicitly. This track aims at providing researchers a platform to share and discuss innovative and advanced learning and assessment technologies utilizing toys, robots, and games.

This track welcomes submissions in topics including, but not limited to:

  • Assessment in the Games and Virtual Worlds
  • Edutainment
  • Educational games
  • Educational robots & toys
  • Innovative interaction in Games
  • Learning Analytics in Educational Games
  • Mobile games and smart city learning
  • Natural and gestural user interface applications (include computer and video games)
  • Serious games
  • Simulation and training (skill, competence, vocational learning)
  • Stealth Assessment
  • Tangible and physical computing for learning
  • Virtual and augmented learning environments

Important dates about ICALT 2017 submissions can be found here.

The ICALT 2017 Author Guidelines can be found here.

The Track 4 CfP can be downloaded from here:

  • .txt version
  • .doc version