孙萌 唐雪萍 郑勤华
（北京师范大学 教育学部，北京 100875）
【摘 要】本研究针对英国开放大学的公开数据集，选择其中的FFF课程的学生行为数据作为研究对象，用滞后序列法分析法对全体以及不同成就水平学生的日行为模式进行了实证探索。研究发现：第一，从日行为模式这一分析角度看，学生的学习行为具有较强的稳定性；第二，高成就组和低成就组的学生在日行为模式的丰富度上有较大差异；第三，学习者在线学习行为中社会性交互活动占有非常重要的地位，但其有效性缺乏保障；第四，低成就水平的学生中，存在着较多的“闲逛”模式，建议教师针对这类学生在平台中设计更加明确的教学活动指引。本研究旨在从日行为模式这一新角度出发，寻找典型的日行为模式，从而为优化教师的教学设计和教学指导提供建议，同时也为在线学习行为的分析提供新的切入点。
【关键词】在线学习；日行为模式；滞后序列分析法；学习行为
The Sequential Analysis of Students’ Behavior Based on Daily Behavior Model
SUN Meng, TANG Xueping and ZHENG Qinhua
(The Faculty of Education, Beijing Normal University, Beijing 100875, China)
Abstract: Based on the open dataset of UK Open University, this study chooses students’behavior data of FFF course as the research object, and uses the lag sequential method to analyze the daily behavior patterns (i.e. the combination of activities that students participate in within one day) of all students and students with different achievement levels. Four main fi ndings of the study are as follows: fi rstly, from the perspective of daily behavior pattern, students’learning behavior has a strong stability; secondly, there are great differences in the richness of daily behavior pattern between high-achievement group and low-achievement group; thirdly, social interaction plays an important role in online learners’learning behavior, but its effectiveness is not guaranteed. Fourthly, there are many “wandering” modes among students with low achievement level. Teachers are advised to design more explicit instructional activities for such students on the platform. The purpose of this study is to find a typical daily behavior model from a new perspective of daily behavior model, so as to provide suggestions for optimizing teachers’teaching design and guidance, and to provide a new entry point for the analysis of online learning behavior.
Keywords: online learning; daily behavior model; Lag Sequence Analysis; learning behavior
下载：
基于日行为模式的学生行为序列分析.pdf
