Date
Publisher
arXiv
With the development of artificial intelligence, personalized learning has attracted much attention as an integral part of intelligent education. In recent years, countries and regions such as China, the United States, and the European Union have increasingly recognized the importance of personalized learning, emphasizing its potential to integrate large-scale education with individualized instruction effectively. This survey provides a comprehensive analysis of personalized learning by reviewing relevant studies published in major conferences and journals between January 2017 and April 2025. We examine its definition, objectives, and underlying educational theories, highlighting its pedagogical significance. Furthermore, we explore personalized learning from two key dimensions: student modeling and personalized recommendations. Student modeling is analyzed from both cognitive and non-cognitive perspectives, while recommendation approaches are categorized based on their specific objectives. Additionally, we investigate the interplay between these components and their role in enhancing personalized learning. Beyond theoretical and algorithmic insights, this survey reviews real-world applications, demonstrating personalized learning's effectiveness in educational practice. Finally, we discuss key challenges and future directions, offering a multidimensional perspective that bridges theory and practice.
Study at a glance
- Question: How do student modeling and personalized recommendations support adaptive learning across educational theories and practice?
- Setting and data: Review of 238 academic articles published between January 2017 and April 2025, primarily covering K-12 and higher education contexts.
- Method: Systematic literature review (four-step retrieval, screening, fine-grained selection, and backward snowballing).
Why it matters
Personalized learning integrates large-scale education with individualized instruction to address student diversity, but existing reviews often focus on narrow aspects rather than systematically linking educational theories, student modeling, and recommendation systems.
Key findings
- Personalized learning research centers on student modeling (cognitive and non-cognitive) and personalized recommendations across learning paths, courses, and exercises.
- Real-world deployments demonstrate gains, including a randomized controlled trial in middle schools showing a Knewton-style adaptive system achieved modest learning gains and a 4.2-percentage-point dropout reduction.
- Emerging large language models shift exercise personalization from selecting pre-existing problems to generating adaptive content, though challenges persist regarding hallucinations, curricular alignment, and data privacy.
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