[1]菅保霞,姜 强,赵 蔚,等.大数据背景下自适应学习个性特征模型研究 ——基于元分析视角[J].远程教育杂志,2017,(04):087-96.[doi:10.15881/j.cnki.cn33-1304/g4.2017.04.009]
 Jian Baoxia,Jiang Qiang,Zhao Wei,et al.Research on Students’ Personality Traits Modeling in Adaptive Learning on the Background of Big Data:Based on the Perspective of Meta-analysis[J].Distance Education Journal,2017,(04):087-96.[doi:10.15881/j.cnki.cn33-1304/g4.2017.04.009]
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大数据背景下自适应学习个性特征模型研究 ——基于元分析视角()
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《远程教育杂志》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2017年04期
页码:
087-96
栏目:
学习新论
出版日期:
2017-07-12

文章信息/Info

Title:
Research on Students’ Personality Traits Modeling in Adaptive Learning on the Background of Big Data:Based on the Perspective of Meta-analysis
作者:
菅保霞; 姜 强; 赵 蔚; 李勇帆
1.东北师范大学 计算机科学与信息技术学院,吉林长春 130117;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):
Jian Baoxia; Jiang Qiang; Zhao Wei; Li Yongfan
1.College of Computer Science and Information Technology, Northeast Normal University, Changchun Jilin 130117;2.Institute of Information Science and Engineering, Hunan First Normal University, Changsha Hunan 410205
关键词:
大数据自适应学习学习分析人工智能技术个性特征模型元分析
Keywords:
Big data Adaptive learning Learning analytics Artificial intelligence technologyPersonality traits model Meta-analysis
分类号:
G434
DOI:
10.15881/j.cnki.cn33-1304/g4.2017.04.009
文献标志码:
A
摘要:
技术作为人的存在方式,正在促使教学模式和学习方式发生深刻变革。大数据时代,在学习分析、人工智能、机器学习等新兴技术支持下,自适应学习系统有助于学习者进行差异化学习,促进教育向个性化迈进。基于文献的元分析视角,对知识水平、错误/误解、情感、认知特征以及元认知能力等个性特征进行分析,并对覆盖法、基于认知理论建模、基于约束的模型、模糊逻辑技术、贝叶斯网络和本体技术等建模方法进行解读。同时,采用适切的建模方法构建学习者个性特征模型,并以“自适应课件导学系统(AC-ware Tutor)”为例,解析学习者模型的运行机制。从而有助于提供精准的个性化学习服务,提高教育质量。
Abstract:
As a way of human existence, technology is making a profound change in teaching patterns and learning style. On the background of big data, artificial intelligence, machine learning and other emerging technologies, adaptive learning system could help students to achieve differentiated learning, and to promote education to develop to the direction of individual learning. Based on the meta-analysis perspective, the paper analyzes learners’ personal characteristics such as knowledge level, errors and misconceptions, cognitive features, affective features, meta-cognitive features and so on. It interprets modeling methods like the Overly Model, Cognitive Theories, Constraint-based Model, Fuzzy Logic, Bayesian Networks and Ontology-Based Model. Meanwhile, it elaborates that using appropriate modeling technique to construct the learner model can help to provide accurate and individualized adaptive learning service and improve the quality of education. At last, the paper takes “AC-ware Tutor” as an example to analyze the operating mechanism of the learn model. The result will be the theoretical basis for the construction of intelligence learning space.

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备注/Memo

备注/Memo:
基金项目:本文系教育部人文社科规划项目“基于知识图谱的开放学习资源自主聚合研究”(项目编号:14YJA880103);教育部人文社科青年项目“大数据时代在线学习者情感挖掘与干预研究”(项目编号:16YJC880046);基础教育信息化技术湖南省重点实验室(项目编号:2015TP1017);湖南省哲学社会科学基金项目“‘互联网+’促进城乡基础教育均衡发展的创新机制与路径研究”(项目编号:16YBA094)资助的阶段性成果。
更新日期/Last Update: 2017-07-13