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- The submission has been prepared in accordance with the journal's Instructions for Authors and follows all the journals' policy requirements.
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Topic: Virtual and Augmented Reality in Education
In the last few years, Virtual reality (VR) and Augmented Reality (AR) have rapidly increased their popularity. AR/VR solutions can enhance classroom experiences and expand opportunities at all levels of learning. As a result, their use in educational environments has been a topic of interest in academic education. However, both virtual reality and augmented reality are not yet widely used in higher education.
This topical collection will introduce the technology of augmented reality (AR) and its possibilities for education and provide a model for the development of integrated learning objects based on the VR and AR approach where VR and AR objects could be integrated into other educational content as learning objects by creating integrated learning programs, learning scenarios.
Topic: Gamification and Game-Based Learning
Gamification is an educational approach that seeks to motivate students by using introduction of game elements into a traditionally non-game situation. Game-based learning refers to the borrowing of certain gaming principles, it is designing learning activities that can incrementally introduce concepts, and guide users towards an end goal. Gamification and game-based learning both promote engagement and sustained motivation in learning.
This topic aims to publish works focused on all aspects of gamification and Game-based learning. It welcomes submissions from academics and researchers that facilitate education or propose new theories on teaching and learning that can be supported by such technology.
Topic: Educational Data Mining (EDM) and Learning Analytics (LA)
Educational data mining (EDM) and learning analytics (LA) are used to represent the application of data mining in higher education and other educational settings. They are fundamentally based on computational data analysis and can consecutively collect, process, report, and work on digital data to improve the educational process. EDM and LA are used to offer more personalized, adaptive, and interactive educational environments to enhance learning outcomes, teaching and learning effectiveness, and optimize institutional proficiency, contributing to both learning sciences and educational theory more broadly.
The topic seeks to connect learning analytics researchers, developers, and practitioners who share a common interest in computational approaches to educational data mining, to better understand and improve learning through the creation and implementation of new tools and techniques.
Specifically, it welcomes high-quality original work including but not limited to:
Social Network Analysis
Analysis and Visualization of Data
Prediction, Clustering, and Relationship Mining
Developing concept maps
Discovering or improving domain models
Predicting students’ future learning behavior
Topic: Artificial Intelligence (AI) in Education
Artificial Intelligence (AI) in education is about using computer intelligence to help teachers and students and make the education system much better and more effective. AI in Education is developing new solutions for teaching and learning in different situations. The potential of AI for education and learning and the role of education in developing AI literacy, have also received increased attention.
The proposed topic collection aims to understand how artificial intelligence enables innovation in education and teaching, changes educational practices, and how major stakeholders in the education industry (i.e. students, teachers, teachers, and families) view this continuous change. Relevant topics include but are not limited to:
- Intelligent Tutoring Systems
- Adaptive learning environments
- Personalized Learning
- Automated Grading
- Chatbots and Virtual Assistants
- Predictive Analytics
- Intelligent techniques for objective and integrated students evaluation in TEL
- Teachers' competencies for effective integration of AI into Education
- Teachers’ perceptions of AI: prejudices and attitudes
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