学习分析视角下自我导向学习多维诊断框架构建与应用

Construction and Application of Multidimensional Self-directed Learning Diagnosis Framework from the Perspective of Learning Analytics

诊断学生的自我导向学习水平是提升其自我导向学习能力的首要前提。在学习分析视角下,该研究将自我导向学习能力和行为作为研究要素,构建了由理论层、数据层、诊断层组成的“自我导向学习多维诊断框架”。在“自导式3D设计”情境中,以193名中学生为研究对象,从描述、解释和预测三个维度诊断自我导向学习,挖掘自我导向学习能力与行为间的交互作用。结果表明,存在4类自我导向学习能力群体;不同能力群体间自我导向学习行为显著差异;自我导向学习行为能显著预测自我导向学习能力。依据研究结果,从创设真实的任务情境、提供开放的学习资源、促进良好的人际沟通、激励反思性学习评价、提供过程性学习支架、提供信息化学习工具和实施适切的支持策略等方面提出了促进自我导向学习的建议。

Diagnosis of students’self-directed learning level is the primary prerequisite to improve their self-directed learning ability.Based on the perspective of learning analytics and taking self-directed learning ability and behavior as the core,this study constructs a“multidimensional self-directed learning diagnosis framework”to explore the interaction between self-directed learning ability and behavior,which consists of theoretical layer,data layer and diagnostic layer.In the context of“self-directed 3D design”with the sample consisting of 193 secondary school students,a multi-dimensional diagnosis of self-directed learning was implemented from three dimensions of description,explanatory and prediction.The results showed that there were four types of self-directed learning ability groups.There were significant differences in self-directed learning behaviors among self-directed learning ability groups.Self-directed learning behavior plays a significant role in predicting self-directed learning ability.According to the research results,recommendations for promoting self-directed learning were made in terms of creating real task contexts,providing open resources,promoting good interpersonal communication,stimulating reflective assessment,providing process scaffolding,providing informatization tools,and implementing appropriate support strategies.