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基于在线学习平台的学习者交互行为研究 被引量:1

A Research on Learners’Interactive Behavior on Online Learning Platform
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摘要 尽管在线学习已得到较为成熟和广泛的实践应用,但关于如何开展高效度、深层次的线上异步教学交互,提升其教学质量方面的研究,尚处于不完备阶段。对学习者交互行为进行研究,可使其对课堂教学行为有更深层次的理解,有利于分析和把握学习者的认知行为、知识建构行为,为提升在线教学质量提供有力的保障。文章从在线平台上选取一门课程,借助交互分析模型(IAM)编码框架,通过滞后序列分析技术对该课程学习者的交互行为数据进行研究,并对不同成绩学习者进行分析,得出如下结论:学习者的整体交互水平大多停留于低水平阶段;高分组学习者的交互频次要多于低分组,交互质量要高于低分组;高分组与低分组的显著性行为序列及特点、学习者的交互效果对其学习成绩有一定程度的影响。最后,针对分析结果提出五点建议。 The research on carrying out efficient and deep-seated online asynchronous teaching interaction to improve teaching quality is still in an incomplete stage,although current online teaching and learning is mature and widely-practiced.The research on learners’interactive behavior can enable us to have a deep understanding of teaching behavior,help us analyze and grasp learners’cognitive behavior and knowledge construction behavior,and provide a strong guarantee to improve the quality of online teaching.This paper selects a course from the online learning platform,uses the interactive analysis modeling framework and lag sequence analysis technology to study the interactive behavior data of learners,and analyze learners with different grades.The research concluded that most interaction level of learners were at the low level stage.Meanwhile,the learners’frequency of interaction at high group is higher than learners at low group,and the interaction quality is higher than learners at low group.Besides,high group and low group had significant behavior sequence and characteristics,and the interaction effect of learners had a certain impact on academic performance.Finally,the paper proposed five suggestions based on results.
作者 张涛 纪璐璐 刘兵倩 王晴 Tao ZHANG;Lulu JI;Bingqian LIU;Qing WANG(School ofInformation and engineering,Henan Institute ofScience and Technology,Xinxiang Henan 453003)
出处 《中国教育信息化》 2022年第3期64-72,共9页 Chinese Journal of ICT in Education
基金 2020年度河南省哲学社会科学规划项目“网络学习空间中学习行为及其对学习效果的影响机理研究”(编号:2020CJY041) 2020年度河南省教育厅人文社科一般项目“基于教育大数据的学习行为分析及实证研究”(编号:2020-ZZJH-152)。
关键词 在线学习 交互分析 IAM编码框架 滞后序列分析法 Online learning Interactive analysis IAM coding framework Lag sequence analysis
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