Smart Technologies for Blended Learning

in Special Issue   Posted on April 7, 2021 

Information for the Special Issue

Submission Deadline: Wed 15 Sep 2021
Journal Impact Factor : 0.000
Journal Name : Progress in Artificial Intelligence
Journal Publisher:
Website for the Special Issue: https://www.springer.com/journal/13748/updates/19035864
Journal & Submission Website: https://www.springer.com/journal/13748

Special Issue Call for Papers:

In recent years, most educational sectors move towards blended learning methods to easily teach learners using smart technologies. Blended learning is a technique which combines smart visual technologies and traditional face-to-face teaching for advanced learning. In general, Blended learning involves both teachers and students’ physical presence, with some aspects of student control over time, position, direction, or pace. Smart Technologies such as Artificial Intelligence (AI), Machine learning (ML), Cloud assistance and Smart Visual technologies are used to improve blended learning. The significance of smart technologies in Blended Learning is to build the most immersive and improved way for learners to learn. The integration of smart technologies and interaction modules with the resources keep students focused for more extended periods than books or paper resources. This engagement helps to develop learning through exploration and research. The use of Deep learning-based eLearning materials strengthens a student’s capacity to set acceptable learning objectives and take responsibility for their learning, which creates a competence that can be translated across all subjects using smart technologies. The associated knowledge gain analysis is assessed and tracked successfully using Smart technologies in blended learning. Functional data collection, Fuzzy-assisted intelligent learning using imaging, is the innovative smart technological method used to efficiently evaluate blended learning. The AI-based smart learning content and tutoring system enabled developing the adaptive learning features using customized resources to enhance the blended learning experience. Cloud assisted Deep Learning technology is a smart evaluation method in blended learning which is used for accessing Real-time students’ performance. Big Data Analytics uses smart technologies to store a large volume of data to analyses and conceptualize the blended learning method’s learning contents. Visual smart technologies such as Augmented reality (AR) and Virtual reality (VR) increases the learners’ understanding and innovating capability in blended learning.

The significant challenges in blended learning is to recognize the psychology of the students, interest and appraisal of their involvement in the e-platform, and the overwork for teachers. In the future, smart technology in blended learning can minimize the Student psychological challenges by prediction and warning method using state of the art techniques. Therefore, the most significant impact of smart technologies in blended learning helps to reduces teachers’ workload. This special issue invites innovative techniques, computational modelling, and algorithms to develop smart technologies for blended learning.

The topics of interest for the special issue:

  • Smart student behavior assessment using Artificial Intelligence (AI) and Big Data Analytics for blended learning
  • Smart Automated assessment of student assignment using AI for blended learning
  • Smart AR Framework for structural studies in blended learning
  • Smart VR Framework concept to enhance the functional studies in blended learning.
  • Smart Students’ involvement analysis using deep learning for blended learning
  • Smart Integration of AI assisted cloud technology for accessing student performance in blending learning
  • Big Data Analysis for student performance prediction for blended learning.
  • Smart textbook for blended learning using imaging technology
  • Deep Learning Technology for computational modelling learning content for blended learning technology.
  • Fuzzy assisted AI technology for smart evaluation and prediction of mathematical solution for Higher education blended learning. 
  • Better Smart image sensing technology for recognizing student and teacher relationship using AI assistance for blended learning.
  • Smart teaching virtual technology for students using DL-based VR technology.

Submission Deadline: September 15, 2021

Guest Editors

Dr. VICENTE GARCÍA DÍAZ                       Associate Professor,                          University of Oviedo, SpainEmail: [email protected]

Dr. Vicente Garcia Diaz is working as an Associate Professor in the Computer Science Department of the University of Oviedo. He obtained his PhD from the University of Oviedo in Computer Engineering. He completed his graduation in Prevention of Occupational Risks and he is a Certified Associate in Project Management through the Project Management Institute. His teaching interests are primarily in the design and analysis of algorithm. He worked as a visiting professor at universities and centers in different countries. His research interests include model-driven engineering, domain specific languages, technology for learning and entertainment, project risk management, software development processes and practices.

Dr. JERRY CHUN-WEI LINProfessor, FIET,Western Norway University of Applied Sciences, Bergen, NorwayEmail: [email protected]

Dr. Jerry Chun-Wei Lin, is working as a Professor at the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway. He worked as an Assistant Professor (2012/10 – 2016/12) and Associate Professor (2016/12 – 2018/07) at School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, China. He has been awarded as the Most Cited Chinese Researcher in 2018 and 2019 in Elsevier. He is the member of IET and senior member of IEEE and ACM. He is the project leader of SPMF and PPSF projects. His research interests include Data mining, Soft Computing, Artificial Intelligence, Privacy-preserving, Optimization and Security Technologies.

Dr. JUAN ANTONIO MORENTE MOLINERAAssociate Professor,                              PDI, University of Granada.Email: [email protected]

Dr. Juan Antonio Morente Molinera, is working as a Professor at the Department of Engineering, School of Engineering and Technology, Universidad International de la Rioja, Logrono, Spain. He obtained his PhD in Information and Communication Technology with a National Research Training Grant from the University of Granada, Spain in 2015.He is part of the Soft Computing and Intelligent Information systems and Mobility and User Experience Research Groups at the University of Granada and UNIR. His research interests include Artificial Intelligence, Fuzzy set theory, Maxwell equations, Bio Informatics, Decision Support System, Knowledge Management, Linguistics, Molecular Biophysics.