| 混合学习环境下学习者的在线自我调节学习潜在剖面分析及行为过程挖掘 |
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| 引用本文： | 邓国民,徐新斐,朱永海.混合学习环境下学习者的在线自我调节学习潜在剖面分析及行为过程挖掘[J].电化教育研究,2021,42(1):80-86. |
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| 作者姓名： | 邓国民 徐新斐 朱永海 |
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| 作者单位： | 贵阳学院教育科学学院,贵州贵阳 550005;贵州师范大学教育学院,贵州贵阳 550025;首都师范大学初等教育学院,北京 100048 |
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| 基金项目： | 教育部人文社会科学研究2019年度一般项目"基于学术社交网络的高校科研影响力计量评价研究";贵州省2017年度本科教学内容和课程体系改革项目"本科师范专业'教育学'公共课混合教学改革研究" |
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| 摘 要： | 混合学习强调线下课堂教学和线上自主学习的混合以实现优势互补,其中学习者的在线自我调节学习能力显得异常重要。文章旨在揭示学习者的在线自我调节学习能力存在哪些潜在类别,不同类别学习者是否具有不同的在线自我调节学习行为过程模型,以及这对于在线自我调节学习环境的设计有何启示。研究首先对239名学习者的在线自我调节学习能力进行测评,然后使用潜在剖面分析方法对测评数据进行分析,发现样本学习者可以分为高、中、低三种不同水平的自我调节学习剖面类别。然后分别对三种类别学习者的在线自我调节学习行为数据进行过程挖掘,研究发现:(1)学习者的自我调节学习能力更多体现在执行阶段的行为上;(2)中高水平自我调节学习者的在线学习行为表现出更强的认知和元认知策略;(3)高水平自我调节学习者体现出更有效的时间管理策略与更强的整体规划能力。因此,在线自我调节学习环境需要引入自适应支持机制,为学习者提供适应性的过程和策略支持。
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| 关 键 词： | 混合学习 自我调节学习 在线自我调节学习环境 潜在剖面分析 过程挖掘 自适应学习 |
Latent Profile Analysis and Behavioral Process Mining of Learners' Online Self-regulated Learning in Blended Learning Environment |
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| DENG Guomin,XU Xinfei,ZHU Yonghai.Latent Profile Analysis and Behavioral Process Mining of Learners' Online Self-regulated Learning in Blended Learning Environment[J].E-education Research,2021,42(1):80-86. |
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| Authors: | DENG Guomin XU Xinfei ZHU Yonghai |
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| Institution: | (School of Education Science,Guiyang University,Guiyang Guizhou 550005;School of Education,Guizhou Normal University,Guiyang Guizhou 550025;School of Elementary Education,Capital Normal University,Beijing 100048) |
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| Abstract: | Blended learning emphasizes the blending of offline classroom teaching and online self-regulated learning to achieve complementary strengths,where learners'online self-regulated learning abilities are extremely important.This paper aims to reveal the latent profiles of learners'online self-regulated learning abilities,whether learners of different categories have different patterns of online self-regulated learning behavioral processes,and what implications this may have for the design of online self-regulated learning environments.This study first assesses online self-regulated learning abilities of 239 learners,and then analyzes the data using the latent profile analysis,and finds that the sample learners can be divided into three types of self-regulated learning profiles with high,medium,and low levels.Then,the process mining of online self-regulated learning behavior data of three types of learners is conducted respectively,and it is found that:(1)learners'self-regulated learning abilities are more reflected in behaviors at the execution stage;(2)Online learning behaviors of middle and high-level self-regulated learners show stronger cognitive and metacognitive strategies;(3)High-level self-regulated learners show more effective time management strategies and stronger overall planning skills.Therefore,online self-regulated learning environments needs to introduce adaptive support mechanisms to provide adaptive process and strategic support for learners. |
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| Keywords: | Blended Learning Self-regulated Learning Online Self-regulated Learning Environment Latent Profile Analysis Process Mining Adaptive Learning |
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