Entropy in Adaptive Learning Systems: Modeling Uncertainty, Information Dynamics and Personalized Education
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 912
Special Issue Editors
Interests: personalization; human–computer interaction; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: social network analysis; multimedia applications; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: semantic analysis; multimedia applications; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: software engineering; educational technology; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The growing complexity of modern learning environments—driven by artificial intelligence, ubiquitous computing, and real-time data—demands new theoretical and computational tools to model, personalize, and optimize learning experiences. Entropy, as a measure of uncertainty, variability, and information flow, offers a powerful lens through which to analyze adaptive learning systems. This Special Issue aims to explore how entropy-based methods and information-theoretic approaches can be harnessed to advance personalized education, learner modeling, and decision-making in intelligent tutoring systems, educational data mining, and learning analytics.
We invite contributions that investigate entropy in the context of modeling cognitive or affective uncertainty, adapting content delivery, optimizing feedback, or enhancing system robustness. Topics may include, but are not limited to, entropy-driven algorithms, uncertainty quantification in learner modeling, information dynamics in dialog-based systems, and hybrid models that integrate entropy with machine learning, fuzzy logic, or other computational intelligence techniques.
This Special Issue seeks to bridge the gap between information theory and educational technologies by showcasing interdisciplinary research that deepens our understanding of learning as a dynamic, data-rich, and uncertain process. Researchers from fields such as AI in education, cognitive science, complex systems, and data-driven pedagogy are especially encouraged to contribute.
Dr. Christos Troussas
Dr. Akrivi Krouska
Dr. Phivos Mylonas
Prof. Dr. 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 250 words) can be sent to the Editorial Office for assessment.
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 single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy 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 2600 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.
Keywords
- entropy-based modeling
- adaptive learning systems
- information theory in education
- uncertainty quantification
- personalized education
- learning analytics
- intelligent tutoring systems
- educational data mining
- information dynamics
- AI in education
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