教育数据科学应用于个性化教学中的探索与实践
The Exploration and Practice of the Application of Educational Data Science in Personalized Teaching
DOI: 10.12677/ae.2024.1481498, PDF,    科研立项经费支持
作者: 何丽萍：南京邮电大学教育科学与技术学院，江苏 南京
关键词: 教育数据科学；心理测量学；个性化教学；数据可视化；Educational Data Science； Psychometrics； Personalized Teaching； Data Visualization
摘要: 个性化教学是教育领域追求的目标，旨在根据每个学生的学习需求、兴趣和能力提供定制化的教学。教育数据科学(Education Data Science, EDS)作为一门新兴的跨学科领域，结合了数据科学、学习科学、心理测量学和教育技术。教育数据科学通过分析和挖掘教育大数据，为实现个性化教学提供了新的视角和工具。本文综述了教育数据科学在个性化教学中的应用，探讨了其理论基础、实践案例、面临的挑战和未来的发展方向。
Abstract: Personalized teaching is the goal of education field, which aims to provide customized teaching according to the learning needs, interests and abilities of each student. As a new interdisciplinary field, Education Data Science (EDS) combines data science, learning science, psychometrics and educational technology. Education data science provides new perspectives and tools for personalized teaching by analyzing and mining education big data. This paper summarizes the application of educational data science in personalized teaching, and discusses its theoretical basis, practical cases, challenges and future development directions.
文章引用：何丽萍. 教育数据科学应用于个性化教学中的探索与实践[J]. 教育进展, 2024, 14(8): 898-907. https://doi.org/10.12677/ae.2024.1481498

参考文献

[1] Cope, B. and Kalantzis, M. (2016) Big Data Comes to School: Implications for Learning, Assessment, and Research. AERA Open, 2, 1-19. [Google Scholar] [CrossRef] 
[2] Baker, R. and Siemens, G. (2014) Educational Data Mining and Learning Analytics. In: Sawyer, R.K., Ed., The Cambridge Handbook of the Learning Sciences, Cambridge University Press, 253-272. [Google Scholar] [CrossRef] 
[3] Williamson, B. (2017) Big Data in Education: The Digital Future of Learning, Policy and Practice. SAGE Publications Ltd., 106. [Google Scholar] [CrossRef] 
[4] 何克抗. 教学代理与自适应学习技术的新发展——对美国《教育传播与技术研究手册》(第四版)的学习与思考之六[J]. 开放教育研究, 2017, 23(5): 11-20.
[5] 李晓庆, 余胜泉, 杨现民, 等. 基于学科能力分析的个性化教育服务研究——以大数据分析平台“智慧学伴”为例[J]. 现代教育技术, 2018, 28(4): 20-26.
[6] 施明毅, 杨光莹, 杜敏, 等. 基于校园行为大数据分析的学生画像系统构建探析[J]. 中国多媒体与网络教学学报(上旬刊), 2020(4): 70-71.
[7] 徐涛. 基于大数据分析的人才培养模式设计[J]. 现代电子技术, 2020, 43(20): 122-125.
[8] 刘清堂, 贺黎鸣, 吴林静, 等. 智能时代的教育文本挖掘模型与应用[J]. 现代远程教育研究, 2020, 32(5): 95-103.
[9] 陈明选, 耿楠. 测评大数据支持下的有效教学研究[J]. 远程教育杂志, 2019, 37(3): 95-102.
[10] 陈明选, 许晓群, 王玉家. 基于教育测评数据分析的教学优化研究[J]. 中国电化教育, 2018(5): 80-89.
[11] 陈明选, 王诗佳. 测评大数据支持下的学习反馈设计研究[J]. 电化教育研究, 2018, 39(3): 35-42, 61.
[12] Charitopoulos, A., Rangoussi, M. and Koulouriotis, D. (2020) On the Use of Soft Computing Methods in Educational Data Mining and Learning Analytics Research: A Review of Years 2010-2018. International Journal of Artificial Intelligence in Education, 30, 371-430. [Google Scholar] [CrossRef] 
[13] Rabelo, A., Rodrigues, M.W., Nobre, C., Isotani, S. and Zárate, L. (2023) Educational Data Mining and Learning Analytics: A Review of Educational Management in E-Learning. Information Discovery and Delivery, 52, 149-163. [Google Scholar] [CrossRef] 
[14] Brusilovsky, P. (2004) Adaptive Navigation Support: From Adaptive Hypermedia to the Adaptive Web and Beyond. Psychology, 2, 7-23.
[15] Hsiao, I. and Brusilovsky, P. (2011) The Role of Community Feedback in the Student Example Authoring Process: An Evaluation of AnnotEx. British Journal of Educational Technology, 42, 482-499. [Google Scholar] [CrossRef] 
[16] Weber, G. and Brusilovsky, P. (2015) ELM-ART—An Interactive and Intelligent Web-Based Electronic Textbook. International Journal of Artificial Intelligence in Education, 26, 72-81. [Google Scholar] [CrossRef] 
[17] Aroyo, L., Bra, P.D., Houben, G. and Vdovjak, R. (2004) Embedding Information Retrieval in Adaptive Hypermedia: IR Meets AHA! New Review of Hypermedia and Multimedia, 10, 53-76. [Google Scholar] [CrossRef] 
[18] AltSchool (2016) AltSchool to Begin Offering Its Personalization Platform to Schools Nationwide.
https://www.businesswire.com/news/home/20161018005695/en/AltSchool-to-Begin-Offering-Its-Personalization-Platform-to-Schools-Nationwide

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