Dear Colleagues,

Current technological advancements create fertile ground for the development of learning technology systems and environments that embrace a high degree of sophistication in their modeling, predicting and diagnostic mechanisms. This need has become more profound due to the COVID-19 pandemic, forcing universities and educational organizations to further incorporate web-based approaches in the teaching process. The proliferation of digital learning has led to a massive growth in educational data. As such, intelligent techniques and data analytics in education are essential to enhance the learning experience and improve the quality of education by introducing new approaches to engage students, increase enrollment, prevent dropouts and boost faculty productivity. In particular, educational data-mining can be used to develop predictive models, such as identify at-risk learners or help instructors (through decision-making systems) redefine their teaching strategy optimizing learning. Moreover, through learning analytics, intelligent curriculum and adaptive content based on students' preferences and skills can be developed providing a recommender system with resource relevant to their profile and learning goals. Intelligence and data analytics can transform models and pedagogical approaches creating innovative digital learning environments that lead students in achieving success and support instructors in tailoring their teaching.

In recent decades, research efforts have focused on the development of intelligent and adaptive web-based educational systems incorporating data analytics. Despite increased research interest, there is still scope for a lot of improvement in the direction of intelligence, learning analytics, personalization and adaptivity in digital education. 

This Special Issue welcomes original research papers as well as review articles and short communications in the afore-mentioned area. Topics of interest include but are not limited to the following:

• Artificial Intelligence in Education;
• Personalization and Adaptivity in Learning Technology Systems;
• Machine/Deep Learning in Learning Technology Systems;
• Collaborative and Group Learning, Communities of Practice and Social Networks;
• Simulation-based Learning and Serious Games;
• Immersive and Virtual Reality Environments;
• Ubiquitous, Mobile and Cloud Learning Environments;
• Empirical Studies of Learning with Technologies;
• Adaptive Support for Learning, Models of Learners, Diagnosis and Feedback;
• Modeling of Motivation, Metacognition and Affect Aspects of Learning;
• Virtual Pedagogical Agents and Learning Companions;
• Ontological Modeling, Semantic Web Technologies and Standards for Learning;
• Privacy and Security in e-Learning Environments;
• Affective Computing and Learning Technology Systems;
• Educational Data Mining;
• Analytics and Causal Modelling in Intelligent Tutoring;
• Decision-Making Systems for Quality Education;
• Educational Recommender Systems;
• Learning Analytics and Decision-Making;
• Data Visualization and Analytics in Education;
• User Acceptance of Learning Technology Systems.

Dr. Christos Troussas
Dr. Akrivi Krouska
Prof. Athanasios Voulodimos
Prof. Cleo Sgouropoulou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Computer-assisted learning
• Intelligent tutoring
• Authoring tools and development methodologies for advanced learning technologies
• Adaptive educational hypermedia systems
• Student–computer interactions
• Tutoring modeling
• Student modeling