A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations

• URL: http://arxiv.org/abs/2402.01666v1
• Date: Mon, 15 Jan 2024 08:49:25 GMT
• Title: A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations
• Authors: Siyu Wu, Yang Cao, Jiajun Cui, Runze Li, Hong Qian, Bo Jiang, Wei Zhang
• Abstract summary: China, the United States, the European Union, and others have put forward the importance of personalized learning. This review provides a comprehensive analysis of the current situation of personalized learning and its key role in education.
• Score: 19.064610936977402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of artificial intelligence, personalized learning has attracted much attention as an integral part of intelligent education. China, the United States, the European Union, and others have put forward the importance of personalized learning in recent years, emphasizing the realization of the organic combination of large-scale education and personalized training. The development of a personalized learning system oriented to learners' preferences and suited to learners' needs should be accelerated. This review provides a comprehensive analysis of the current situation of personalized learning and its key role in education. It discusses the research on personalized learning from multiple perspectives, combining definitions, goals, and related educational theories to provide an in-depth understanding of personalized learning from an educational perspective, analyzing the implications of different theories on personalized learning, and highlighting the potential of personalized learning to meet the needs of individuals and to enhance their abilities. Data applications and assessment indicators in personalized learning are described in detail, providing a solid data foundation and evaluation system for subsequent research. Meanwhile, we start from both student modeling and recommendation algorithms and deeply analyze the cognitive and non-cognitive perspectives and the contribution of personalized recommendations to personalized learning. Finally, we explore the challenges and future trajectories of personalized learning. This review provides a multidimensional analysis of personalized learning through a more comprehensive study, providing academics and practitioners with cutting-edge explorations to promote continuous progress in the field of personalized learning.

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