Adaptive E-Learning Technologies and Experiences
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 July 2025 | Viewed by 4583
Special Issue Editors
Interests: NonSQL databases; analytics; e-learning; self-determined learning; eHealth; semantics
Special Issues, Collections and Topics in MDPI journals
Interests: NonSQL databases; machine learning; artificial intelligence; e-learning; programming languages
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Adaptive learning is an innovative approach that focuses on personalizing the learning experience for each student, adapting to their individual needs and learning styles. Adaptive learning may use technologies, algorithms, and analytics to design learning experiences tailored to each student, which can significantly improve learning outcomes and student motivation. Even though, in recent years, adaptive learning has advanced due to new technologies and the increase in distance learning activity due to lockdown, the use of generative intelligence poses new opportunities while creating new issues related to its use within learning experiences.
For this Special Issue, we invite contributions that showcase the convergence of learning design with the implementation of innovative technologies to cultivate purposeful, inclusive, and personalized learning experiences. Furthermore, we encourage explorations into the symbiotic relationship between generative intelligence and e-learning, envisioning the transformative potential this alliance holds for the future of education.
We welcome submissions on a variety of topics, including, but not limited to, the following:
- Experiences in adaptive learning;
- Integration of machine learning algorithms into online learning platforms;
- Impact of adaptive learning on student retention and achievement;
- Challenges and limitations in the implementation of adaptive learning;
- Evaluation and optimization of machine learning algorithms;
- Impact on higher education;
- Personalization of content and pace of learning;
- Data analysis and real-time feedback;
- Integration with other educational systems and tools;
- Evaluation of effectiveness and efficiency;
- Development of skills and competencies.
Authors are encouraged to submit their research articles, case studies, and insights on these topics to advance our understanding of how these technologies can shape the future of e-learning.
Dr. Jordi Conesa Caralt
Prof. Dr. Antonio Sarasa Cabezuelo
Guest Editors
Manuscript Submission Information
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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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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
- adaptive learning
- e-learning
- personalization of learning
- self-determined learning
- artificial intelligence
- machine learning
- real-time feedback
- analysis of data
- learning algorithms
- learning analytics
- online learning platforms
- skill development
- artificial intelligence
- retrieval augmented generation
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