Personalised and Adaptive Learning for Improved Learning Outcomes
A special issue of Education Sciences (ISSN 2227-7102).
Deadline for manuscript submissions: 1 March 2027 | Viewed by 104
Editors
2. Graduate School of Education, The University of Western Australia, Perth, WA 6009, Australia
Interests: assessment and evaluation; social psychology of education
Special Issue Information
Dear Colleagues,
Education is changing as digital tools and data make it easier to tailor learning to individual students. Around the world, there is growing interest in approaches that better match each learner’s needs, pace, and progress. This shift has been accelerated by online and blended learning, as well as the need for continuous learning in a fast-changing world.
While educators need practical ways to use these approaches at scale, ensure fairness, and protect student data, research gaps remain. There is wide variation in how “personalised” and “adaptive” learning are defined, applied, and evaluated, making it difficult to compare findings and build a clear evidence base. Questions also remain about how these approaches can be scaled, especially in low- and middle-income contexts, where cost, infrastructure, and policy barriers are significant. The role of educators also remains underexplored, particularly how they can be supported to effectively use and contribute to personalised and adaptive systems. Clearly, there is a need for a more coherent and actionable evidence base on what works and how personalised and adaptive learning can improve outcomes.
This Special Issue aims to present and share recent research on personalised and adaptive learning for improving learning outcomes. We welcome contributions that address current gaps and explore how these approaches are defined, designed, implemented, and evaluated across different educational settings.
Topics of interest for publication include, but are not limited to, the following:
- Definitions and frameworks for personalised and adaptive learning;
- Design and implementation of personalised learning approaches;
- Adaptive learning technologies and tools;
- Use of data and learning analytics to support learning;
- Alignment between learning goals, measurement, and outcomes;
- Scalability, cost, and policy considerations;
- Educator roles and professional development;
- Evaluation and impact of personalised/adaptive learning on student outcomes.
Dr. Lyndon Lim
Dr. Che Yee Lye
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 double-anonymized 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 2000 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
- personalised learning environments
- adaptive learning systems
- learning analytics and educational data mining
- artificial intelligence in education
- adaptive assessment and feedback
- student modelling and learner profiling
- data-driven instruction
- equity and ethics in educational technology
- teacher professional development
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