Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment

被引:108
|
作者
Peng, Hongchao [1 ]
Ma, Shanshan [2 ]
Spector, Jonathan Michael [2 ]
机构
[1] East China Normal Univ, Fac Educ, Dept Educ Informat Technol, Shanghai, Peoples R China
[2] Univ North Texas, Coll Informat, Learning Technol, Denton, TX 76203 USA
关键词
Smart learning environment; Data decision-making; Personalized learning; Adaptive learning; Man-machine collaboration; Learning paths recommendation;
D O I
10.1186/s40561-019-0089-y
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Smart devices and intelligent technologies are enabling a smart learning environment to effectively promote the development of personalized learning and adaptive learning, in line with the trend of accelerating the integration of both. In this regard, we introduce a new teaching method enabled by a smart learning environment, which is a form of personalized adaptive learning. In order to clearly explain this approach, we have deeply analyzed its two pillars: personalized learning and adaptive learning. The core elements of personalized adaptive learning and its core concept are explored as well. The elements are four: individual characteristics, individual performance, personal development, and adaptive adjustment. And the core concept is referred to a technology-empowered effective pedagogy which can adaptively adjust teaching strategies timely based on real-time monitoring (enabled by smart technology) learners' differences and changes in individual characteristics, individual performance, and personal development. On this basis, A framework of personalized adaptive learning is also constructed. Besides, we further explored a recommendation model of the personalized learning path. To be specific, personalized adaptive learning could be constructed from the following four aspects, namely, learner profiles, competency-based progression, personal learning, and flexible learning environments. Last, we explored a form of learning profiles model and a generative paths recommendation pattern of personal learning. This paper provides a clear understanding of personalized adaptive learning and serves as an endeavor to contribute to future studies and practices.
引用
收藏
页数:14
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