[1]刘清堂,王 洋,雷诗捷,等.教育大数据视角下的学习分析应用研究与思考[J].远程教育杂志,2017,(03):071-77.[doi:10.15881/j.cnki.cn33-1304/g4.2017.03.008]
 Liu Qingtang,WangYang,Lei Shijie,et al.The Application Study of Learning Analytics Supported by Educational Data[J].Distance Education Journal,2017,(03):071-77.[doi:10.15881/j.cnki.cn33-1304/g4.2017.03.008]
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教育大数据视角下的学习分析应用研究与思考()
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《远程教育杂志》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2017年03期
页码:
071-77
栏目:
学术视点
出版日期:
2017-05-15

文章信息/Info

Title:
The Application Study of Learning Analytics Supported by Educational Data
作者:
刘清堂; 王 洋; 雷诗捷; 张 思
1.华中师范大学 信息化与基础教育均衡发展协同创新中心,湖北武汉 430079;2.湖南第一师范学院 信息科学与工程学院,湖南长沙 410205
Author(s):
Liu Qingtang; WangYang; Lei Shijie; Zhang Si
1.Collavorative & Innovative Center for Balanced Development of Information and Foundation Education, Central China Normal University, Wuhan Hubei 430079;2.Institute of Information Science and Engineering, Hunan First Normal University, Changsha Hunan 410205
关键词:
教育大数据学习分析教育数据挖掘应用研究
Keywords:
Educational big data Learning analytics Educational data mining Application study
分类号:
G420
DOI:
10.15881/j.cnki.cn33-1304/g4.2017.03.008
文献标志码:
A
摘要:
近年来,倡导个性化教学的呼声不断增大,作为个性化教学的先导,学习分析技术是广大教育研究者关注的焦点。采集学习者学习相关数据并进行处理分析,用于预测、评估和优化教与学,为实施个性化教学提供有效的技术支撑是学习分析技术的核心。从国内学习分析的研究现状以及国内外相关模型来看,学习分析模型应是一个包含数据收集、数据分析、评估预测与干预这三个阶段的循环结构。学习分析评估、预测与优化教学的应用策略为:以学习者数据与学习环境数据为基础,对学习者知识掌握程度进行评估;根据学习者历史学习数据,对其学习发展趋势进行预测分析;以评估预测结果为导向,及时实施教学干预、优化教学过程。它为大数据支持下的学习分析应用研究提供一种参考思路。
Abstract:
With the proposition of personalized learning in recent years, learning analytics as the premise of personalized teaching has been the focus of the general education workers. Based on the domestic research status and the relevant models of learning analytics, the model of learning analytics should be a loop structure which embraces data collecting, data analysis, evaluation, forecast and intervention. The learning analytics’ application strategy, namely, evaluation, forecast and optimization of teaching and learning should be carried out as follows. Evaluating the users’ degree of knowledge based on their learning history and environmental data. And according to their learning data, forecasting their development trend of learning. Intervening the teaching and learning, optimizing teaching process in time guided by the results of evaluation and prediction. It may provide a different way of thinking to learning analytics application research based on educational big data.

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备注/Memo

备注/Memo:
基金项目:本文系国家自然科学基金项目“面向Web信息的知识融合关键技术研究”(61272205)、教育部人文社会科学研究青年基金项目“教师网络研修社区中知识共享机制研究”(15YJC880134)、华中师范大学中央高校基本科研业务费项目(CCNU16JCZX05,XJT20150006)的研究成果。
更新日期/Last Update: 2017-05-15