ICALT 2023, Utah, USA


Deadline: January 13th, 2023


Track description and topics of interest

The teaching and learning process is no longer limited to classrooms and could be in remote environments like homes, museums, and parks. Thus, disruptive changes in learning and instruction through emerging technologies require new perspectives for the design and development of learning environments. Closely linked to the demand of new approaches for designing and developing learning environments is the necessity for enhancing the design and delivery of assessment systems and automated computer-based diagnostics. These systems need to accomplish specific requirements, such as adaptivity to different subject domains, flexibility for experimental and instructional settings, managing huge amounts of data, rapid and near real-time analysis of specific data, immediate feedback for learners, educators and learning designers, as well as generating automated reports of the diagnostics’ results that may be useful for educators to provide help for at-risk learners and reduce the dropping-out rate. Furthermore, sophisticated databases and network technologies hold potential for an especially wide variety of applications for technology-enhanced assessment. Hence, given the recent developments in educational data mining and learning analytics, technology-enhanced assessment may improve on-going learning by providing instant and rich feedback on the current stage of the learning process.

This track aims to provide insights into the latest developments of research focusing on technology-enhanced assessment in formal and informal education (either formal or informal education, and the combination of them) by addressing some questions including: “How can we enhance the assessment process using new technologies?”,  “Can learners’ traces be useful in the assessment process?”, “Can emerging technologies in education, such as games and gamification, be used as assessment tools?”, “How can teachers use the real-time analyses to help at-risk learners?”,”How can teachers create an adaptive assessment tool based on the teaching domain?”, “How can the teacher manage the huge amount of data in online learning systems?”, “How can we ensure learners’ privacy protection in e-assessment?”

The scope of the track 14 includes but is not limited to the followings:


  • Automated Feedback to Improve Student Writing
  • Artificial Intelligence in Assessment Processes
  • Natural Language Processing in Alignment of Curriculum and Assessment
  • Enhancing Grading, Scoring and Feedback
  • Computational Models of Knowledge and Expertise
  • Bridging Informal and Formal Learning Outcomes
  • Enhancing Team-Based Learning
  • Game-based Learning
  • Visualization of Learning Processes
  • Exploratory Research into Technology Enhanced Assessment
  • Challenges of Dynamic Learning Analytics
  • Modeling Cognitive, Behavioral and Emotional Processes in Learning
  • New Tools, Processes and Applications that Enhance Assessment
  • Technology-enhanced Assessment of Deeper Learning
  • Peer Assessment Tools and Strategies
  • Electronic Forum Analysis and Assessment
  • Authentic Assessments and Digital Environments
  • Assessment Technologies
  • Security and Privacy in eAssessment
  • Mobile Learning Assessment

We welcome various topics related to TeASSESS !!

Program Committee Member

  • Wilkerson L. ANDRADE, Federal University of Campina Grande, Brazil
  • Adrian BESIMI, SEE University, Macedonia
  • Li CHEN, Faculty of Arts and Science, Kyushu University, Japan
  • Hironori EGI, The University of Electro-Communications, Japan
  • Claudio FREITAS, Purdue Fort Wayne, United States
  • Yuki FUKUYAMA, Kwansei Gakuin University, Japan
  • Ed GEHRINGER, North Carolina State University, United States
  • Yoshiko GODA, Kumamoto University, Japan
  • Nehal HASNINE, Hosei University, Japan
  • Maria José HERNÁNDEZ-SERRANO, University of Salamanca, Spain
  • Ryohei IKEJIRI, The University of Tokyo, Japan
  • Jana JACKOVA, Catholic University in Ružomberok, Slovakia
  • Nobuhiko KONDO, Tokyo Metropolitan University, Japan
  • Siu Cheung KONG, The Education University of Hong Kong, Hong Kong
  • Miloš KRAVČÍK, German Research Center for Artificial Intelligence (DFKI), Berlin, Germany
  • Min LU, Akita University, Japan
  • Khemaja MAHA, ISSAT Sousse Tunisia, Tunisia
  • Ivana MARENZI, L3S Research Center, Germany
  • Hideya MATSUKAWA, Tohoku University, Japan
  • Tsubasa MINEMATSU, Kyushu University, Japan
  • Laurent MOCCOZET, University of Geneva, Switzerland
  • Yosra MOURALI, Université Polytechnique Hauts-de-France, France
  • Claudia PINTO PEREIRA, Universidade Estadual de Feira de Santana, Brazil
  • Jorge SIMÕES, Instituto Superior Politécnico Gaya, Portugal
  • Thomas STAUBITZ, Hasso Plattner Institute for Digital Engineering gGmbH, Germany
  • Aroua TAAMALLAH, isitcom, Tunisia
  • Yuta TANIGUCHI, Kyushu University, Japan
  • Marco TEMPERINI, Sapienza University of Rome, Italy
  • Hanadi TRAIFEH, Hasso Plattner Institute, Germany
  • Mio TSUBAKIMOTO, Tokyo Metropolitan University, Japan
  • Yunkai XIAO, North Carolina State University, United States
  • Jiangmei YUAN, West Virginia University, United States
  • Lanqin ZHENG, Beijing Normal University, China