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

• Published in: Smart learning environments Vol. 6; no. 1; pp. 1 - 14
• Main Authors: Peng, Hongchao, Ma, Shanshan, Spector, Jonathan Michael
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 18.09.2019; Springer Nature B.V; SpringerOpen
• Subjects: Adaptive learning; Capital goods; Computers and Education; Data decision-making; Education; Educational Environment; Educational Strategies; Growth models; How to Make Learning Smart; Individual Characteristics; Learning; Learning paths recommendation; Man-machine collaboration; Personal development; Personalized learning; Recommender systems; School environment; Smart learning environment; Teaching methods; Learning paths recommendation; Man-machine collaboration; Adaptive learning; Smart learning environment; Personalized learning; Data decision-making
• DOI: 10.1186/s40561-019-0089-y
• ISSN: 2196-7091

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.