Computational Intelligence and Neuroscience

Recent Advances in Learning Theory


Status
Published

Lead Editor

1Columbia University, New York, USA

2Nicolaus Copernicus University, Torun, Poland

3Universiti Teknologi Malaysia, Johor, Malaysia

4Louisiana State University, Baton Rouge, USA


Recent Advances in Learning Theory

Description

Learning theories are conceptual frameworks that describe how information is absorbed, processed, and retained during learning. Cognitive, emotional, and environmental influences, as well as prior experience, all play a part in how understanding, or a world view, is acquired or changed and knowledge and skills are retained.

With the rapid development of modern information technology, mobile communication network, and the Internet, the learning environment has taken great changes, which is not only bringing about the new patterns of seamless, ubiquitous learning, but also facilitating the acquisition of the information about the learners’ interest, behaviors, and emotions. On the other hand, the advanced experimental technologies such as fMRI, ERPs, EEG, and wearable equipment are assisting us to explore the neural activities and mechanism in the new learning patterns as well as under the real social environment.

Modern learning theory is developing on the basis of social neuroscience, ubiquitous environment, and the related computational intelligence. The aim of this special issue is to bring together researchers working in different areas to exchange and share the progress in the new tendency of modern learning theory and application. We welcome theoretical contributions, innovative applications, and carefully evaluated empirical studies, and we particularly welcome work that combines all of these elements.

Potential topics include, but are not limited to:

  • Neural mechanism and social psychology in ubiquitous learning
  • Computational model of interest and behavior
  • Affective computing and emotional intelligence
  • Constructivism learning theory, cognitive flexibility theory, and situated learning
  • Team learning, social learning, and learning ecosystem
  • Smart learning based on neural mechanism and context awareness
  • Cyberphysical system and perceptual computing in ubiquitous learning
  • Computational intelligence in learning and learning services
  • Human machine interaction in learning system
  • Comparative studies of deep learning methods
Computational Intelligence and Neuroscience
 Journal metrics
Acceptance rate28%
Submission to final decision79 days
Acceptance to publication37 days
CiteScore5.400
Journal Citation Indicator0.630
Impact Factor3.633
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.