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基于隐马尔可夫模型的学习者在线自我调节学习过程挖掘——时间动力学视域下的分析 被引量:3

Online Self-regulated Learning Process Mining for Learners Based on Hidden Markov Model--Analysis from the Perspective of Temporal Dynamics
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摘要 自我调节学习是随着时间推移变化的动态过程,对学习者在线学习有重要影响。其中,时间性是理解学生如何调节学习的关键因素。目前,在线学习中学习者自我调节学习过程如何激活,如何随时间动态变化的研究较少。因此,文章提出自我调节学习的时间动力学框架,并基于不同时间尺度,使用隐马尔可夫模型识别不同类型学习者自我调节过程差异,从变化类型、时间特征和时间模式三个维度分析学习者自我调节过程的动态变化与差异形成原因。结果表明:(1)自我调节学习的时间动力学框架能有效识别学习者自我调节的动态过程和差异;(2)表现好的学习者自我调节过程阶段性交互作用较强,调节效果较好,时间模式适应性调整特征突出,特别是计划有效性、监控作用方式和评价后的调节倾向方面。研究拓展了隐马尔可夫模型在自我调节学习中的应用,对自我调节学习的时间动力学研究发展具有理论和方法意义,为学习者自我调节过程干预提供支持。 Self-regulated learning is a dynamic process that changes over time and has a significant impact on learners'online learning success.Temporality is a key factor in understanding how students regulate their learning.Currently,there is less research on how learners'self-regulated learning processes are activated in online learning and how they change dynamically over time.Therefore,this study proposes a temporal dynamics framework for self-regulated learning and uses Hidden Markov Model to identify differences in self-regulated processes of different types of learners based on different time scales.The dynamic changes of learners'self-regulation processes and the reasons for the differences are analyzed in three dimensions:types of changes,temporal characteristics,and temporal patterns.The results show that:(1)the temporal dynamics framework of self-regulated learning can effectively identify the dynamic processes and differences of learners'self-regulation;(2)Learners with good performance have stronger stage interactions in their self-regulation processes,better regulation effects,and prominent adaptive adjustment features of temporal patterns,especially in terms of plan effectiveness,monitoring mode of action,and post-evaluation regulation tendencies.This study extends the application of Hidden Markov Model in self-regulated learning,which has theoretical and methodological implications for the development of time-dynamic research on self-regulated learning.And this study provides support for the intervention of learners'self-regulation processes.
作者 乔丽方 赵蔚 段红 徐晓青 QIAO Lifang;ZHAO Wei;DUAN Hong;XU Xiaoqing(School of Information Science and Technology,Northeast Normal University,Changchun Jilin 130117)
出处 《电化教育研究》 CSSCI 北大核心 2022年第10期57-64,共8页 E-education Research
基金 全国教育科学规划课题2019年度国家社科基金教育学一般课题“学习分析支持自我调节学习的效能评价与优化机制研究”(课题编号:BCA190076)。
关键词 自我调节学习 时间动力学 隐马尔可夫模型 潜在剖面分析 学习管理系统 Self-regulated Learning Temporal Dynamics Hidden Markov Model Latent Profile Analysis Learning Management System
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