【作 者】：
李 月， 姜 强， 赵 蔚
【关 键 词 】：
自主学习； 过程挖掘； 学习行为模式； 干预； 回溯行为
【栏 目】：
网络教育
【中文摘要】：
自主学习符合人类学习的本质，被赋予了从未有过的价值与使命，实现了自我驱动的学习行为。在线学习背景下，有必要挖掘学生自主学习行为模式，识别学生认知过程，寻找在线学习规律，解决自主学习能力薄弱问题。通过顺序分析和聚类分析算法，对论坛、测验、作业与评价和资源四类学习行为进行挖掘，分析高低成就学习者之间的在线自主学习行为模式差异。并基于模仿榜样理念，以高成就学习者的学习行为模式为策略，对低成就学习者进行干预。研究结果表明高低成就学习者在线自主学习行为模式存在显著差异，且高成就学习者的学习行为模式对低成就学习者具有指导作用，可为均衡化教学提供新思路与方法，缩小规模化教学与个性化教学的矛盾。
【英文摘要】：
Self-regulated learning conforms to the nature of human learning, and is endowed with unprecedented values and missions, thus realizing self-driven learning behavior. In the context of online learning, it is necessary to mine the behavior patterns of students' self-regulated learning, identify learners' cognitive processes, find online learning rules, and solve the problem of weak ability of autonomous learning. Through sequential analysis and cluster analysis algorithm, this paper excavates four types of learning behaviors ( forums, quizzes, assignments and assessments and resources) and analyzes the differences in online self-regulated learning behavior patterns between high- and low- achievement learners. Based on the idea of imitating the example, this paper takes the learning behavior patterns of high-achievement learners as the strategy to intervene the low-achievement learners. The results show that there are significant differences in the online self-regulated learning behavior patterns of high- and low-achievement learners. And the learning behavior patterns of high-achievement learners have a guiding role for low-achievement learners, which can provide new ideas and methods for balanced teaching and reduce the contradiction between large-scale learning and personalized learning.