A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917225884024832 |
|---|---|
| author | Wu, Siyu Cao, Yang Li, Runze Cui, Jiajun Qian, Hong Jiang, Bo Zhang, Wei |
| author_facet | Wu, Siyu Cao, Yang Li, Runze Cui, Jiajun Qian, Hong Jiang, Bo Zhang, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01666 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations Wu, Siyu Cao, Yang Li, Runze Cui, Jiajun Qian, Hong Jiang, Bo Zhang, Wei Computers and Society 68-02 A.1 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. |
| title | A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations |
| topic | Computers and Society 68-02 A.1 |
| url | https://arxiv.org/abs/2402.01666 |