【作 者】：
彭红超， 祝智庭
【关 键 词 】：
数据决策； 个性化学习； 自适应学习； 适性学习； 人机协同； 精准教学
【栏 目】：
理论探讨
【中文摘要】：
大数据技术的兴起萌发了个性化适性学习的新型学习方式。研究从个性化学习与适性学习理念的对比中，解析出它的核心要素：个体特征、个人表现、个人发展、适性调整。基于核心要素进一步界定了个性化适性学习的核心理念：通过技术赋能，实时监测学习者在个体特征、个人表现、个人发展方面的差异与变化，基于这些差异与变化及时地适性调整教学方略，从而实现有效教学。之后，研究从个性化适性学习的两大支柱——“个性”“适性”出发，构建了它的理念框架，并从人机协同理念的角度出发，详细论述了个性化适性学习的数据决策策略，构建了一种人机协同决策支持的适性调整教学策略的方案谱系。最后，文章建议个性化适性学习的实施可从学习者画像、能基发展、个人学习路径、柔性学习环境四个方面切入，并详细解读了一种面向精准教学的个性化适性学习实施策略。希望研究可以为学者进行后续研究以及教育工作者探究实践途径提供参考。
【英文摘要】：
The emerging of big data has sprouted a new learning method, personalized adaptive learning. Based on the comparison between personalized learning and adaptive learning, this paper analyzes its core elements: individual characteristics, individual performance, personal development, and adaptive adjustment. Based on those core elements, this paper further defines the core concept of personalized adaptive learning: through technology enablement, learners' differences and changes in individual characteristics, individual performance and personal development can be monitored in real time, and teaching strategies can be adjusted timely to achieve effective teaching. Then, from the two pillars of personalized adaptive learning: "personality" and "adaptability", this paper constructs its conceptual framework. Based on the concept of human-machine collaboration, this paper discusses the strategies of data decision-making of personalized adaptive learning in detail, and constructs a scheme pedigree of adaptive adjustment of teaching strategies for human-machine collaborative decision support. Finally, this paper suggests that the implementation of personalized adaptive learning can begin from four aspects: learner profiles, competency-based development, personal learning, and flexible learning environments, and explains in detail an implementation strategy of personalized adaptive learning for precision instruction. It is hoped that this study can provide references for scholars to conduct follow-up studies and educators to explore practical approaches.
【下 载】：
人机协同决策支持的个性化适性学习策略探析