基于大数据学习分析的在线学习绩效预警因素及干预对策的实证研究

发布时间：2018-03-19 17:40

本文选题：教育大数据　切入点：学习分析　出处：《电化教育研究》2017年01期 　论文类型：期刊论文

【摘要】：利用学习分析技术挖掘教育大数据,能够发现潜在价值,并使其转换成有意义的教学信息,进而优化学习过程、提高教学效果,成为教师、学生及教育研究者的共同诉求。从学习分析技术的教育应用视角,追踪、积累并筛选在线学习行为数据,利用多元回归分析法判定影响学生学习绩效的预警因素,在此基础上构建了干预模型,将其应用于教学实践中,对产生的学习行为数据进行二元Logistic回归分析,并结合问卷调查和访谈法对该模型在学习活动、知识习得等方面的有效性进行验证。结果表明,通过学习过程中实施的干预模型识别出存在学习危机的学生,及时向其发出预警信号并提供个性化干预对策,有利于增强学习动机,培养学习毅力,提高学习质量。
[Abstract]:By using learning analysis technology to excavate educational big data, we can discover the potential value and transform it into meaningful teaching information, and then optimize the learning process, improve the teaching effect and become a teacher. From the perspective of educational application of learning analysis technology, tracking, accumulating and screening online learning behavior data, using multiple regression analysis method to determine the early warning factors affecting students' learning performance. On this basis, the intervention model is constructed and applied to the teaching practice. The binary Logistic regression analysis is carried out on the resulting learning behavior data, and a questionnaire survey and interviews are used to analyze the learning activities of the model. The effectiveness of knowledge acquisition is verified. The results show that students with learning crisis are identified by the intervention model in the process of learning, and they are given early warning signals and individualized intervention countermeasures in time. It is beneficial to enhance learning motivation, cultivate learning perseverance and improve learning quality.
【作者单位】：东北师范大学计算机科学与信息技术学院;湖南第一师范学院信息科学与工程学院;长春教育学院;
【基金】：教育部人文社会科学研究规划基金项目“基于知识图谱的开放学习资源自主聚合研究”(项目编号:14YJA880103) 基础教育信息化技术湖南省重点实验室(项目编号:2015TP1017)
【分类号】：G434

【相似文献】