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Jingdong book

量化学习：数据驱动下的学习行为分析

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Jingdong book

量化学习：数据驱动下的学习行为分析

Name: 量化学习：数据驱动下的学习行为分析
Brand: Jingdong book
SKU: 3118223591
Price: 45.46 USD
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信息技术助力下的教育已迎来量化时代。数据驱动下的学习行为分析研究跨越了教育领域的多个研究场景，以量化学习者在不同情境下的学习过程作为出发点，为解决数字化情境下个体和群体学习行为数据的采集、挖掘、分析和应用提供一系列方法和策略。本书系统地介绍了量化学习这一全新的教育科学研究范式，从研究的背景、应用领域以及研究方法三个方面展开了全面论述。在研究方法中，分别阐述了学习行为数据采集的场景、标准与规范，以及个体和群体学习行为分析的理论、方法与实践经验，同时在方法介绍中提供了具体的研究案例，并对案例所涉及的实证研究策略和结论展开了深入的探讨。

Content Description

Author Description

刘三女牙，1973年出生，中共党员，教授，博士生导师，教育部新世纪优秀人才支持计划和湖北省新世纪高层次人才入选者，现任国家数字化学习工程技术研究中心常务副主任，教育部基础教育“未来教室装备”研究中心常务副主任，中国教育发展战略学会教育信息化专业委员会专家，教育部教育信息化专家库专家，湖北省系统工程学会常务理事和湖北省青年科技工作者协会理事。主要研究方向为计算机应用、人工智能和教育信息技术。长期从事信息技术与教育教学融合的科学研究工作，面向未来数字化学习的需求，进行关键技术攻关和理论创新。

Catalogue

内容简介

前言

量化学习

数据对教育的重要性

学习数据的隐私与安全

本书导读

致谢

第一部分概述

第1章学习行为分析

1.1 教育与大数据同行

1.2 学习分析方兴未艾

1.3 从量化自我到量化学习

1.4 数据驱动的学习行为分析

第2章学习行为分析应用

2.1 个性化学习诊断

2.2 学习路径规划

2.3 个性化学习推荐

2.4 学习状态可视化

2.5 学习干预

第二部分学习过程的量化

第3章量化方法

3.1 物理空间 28

3.1.1 课堂学习

3.1.2 户外学习

3.2 虚拟空间

3.2.1 学习管理系统

3.2.2 大规模在线开放课程

3.2.3 智能导师系统

3.2.4 面向资源交互的学习过程量化

3.2.5 游戏化学习情境

第4章数据标准与规范

4.1 学习分析标准和规范

4.2 学习活动规范

4.2.1 xAPI学习经历规范

4.2.2 面向学习情境的交互规范

4.3 面向不同学习系统的学习经历规范

4.3.1 面向LMS的跨平台数据规范

4.3.2 面向大规模在线开放课程的数据规范

4.3.3 面向智能导师系统的数据规范

4.4 面向资源交互的数据规范

第三部分个体学习行为分析

第5章学习效果预测

5.1 简介 57

5.2 基于统计分析的学习效果预测

5.3 基于统计机器学习的学习效果预测

5.4 基于时间序列分类的学习效果预测

第6章知识能力分析

6.1 简介

6.2 基于标准化测验的知识等级定位

6.3 基于交互式测验的高等能力评量

6.4 基于概念构图法的知识结构建立

6.5 基于自动文章评分的文本回应分析

第7章认知风格分析

7.1 简介

7.2 基于问卷和心理计量测验的认知风格分类

7.3 基于神经网络方法的认知风格识别

7.4 基于模糊分类树的认知风格预测

第8章学习兴趣挖掘

8.1 简介

8.2 基于自报告的学习兴趣挖掘

8.3 基于在线点击行为的学习兴趣挖掘

8.4 基于在线文本内容的学习兴趣挖掘

第9章情感状态识别

9.1 简介 99

9.2 基于量表的情感测评方法

9.3 基于生理信号的情感检测方法

9.4 基于面部表情的情感识别方法

9.5 基于语音信号的情感检测方法

9.6 基于文本数据的情感分析方法

第四部分群体学习行为分析

第10章群体学习动力学分析

10.1 简介

10.2 数据化的群体动力学研究—社会网络分析

10.3 基于学习者自报告的社会网络分析法

10.4 基于在线行为统计的社会网络分析方法

第11章群体学习行为模式分析

11.1 简介

11.2 基于聚类方法的学习行为模式识别

11.3 基于序列分析的学习行为分析

11.4 基于序列模式挖掘的学习行为分析

11.5 基于眼动追踪方法的眼动行为分析

第12章协作学习及参与行为分析

12.1 简介

12.2 基于物理课堂的小组协作学习

12.3 基于计算机辅助教学的协作学习

12.4 基于移动技术支持的课堂协作学习

12.5 基于游戏场景下的协同学习

第13章互动话语行为分析

13.1 简介

13.2 基于语音记录的话语分析方法

13.3 基于姿势识别的话语分析方法

13.4 基于文本交互的话语分析方法

13.5 基于ITS人机智能对话分析

第五部分后记

Introduction

当前，以互联网为代表的信息技术已经成为社会进步和发展的重要推动力量，正在全面重塑传统行业，持续地颠覆人们的既有认知。面对汹涌而至的数字化浪潮，教育正面临着来自理论和实践两大层面的挑战：理论界和实践者对教育更加科学化和精确化的追求。随着数字化时代的深入发展，人类开展科学研究和社会实践的思维方式正在不断演进，技术助力下的数据密集型科学研究范式和数据素养引起了人们的广泛关注，各领域在秉承既有认知模式（如基于实际经验的认知模式）的同时，高度重视基于量化信息的数据化认知模式。信息技术，特别是移动互联、物联网、云计算、人工智能等的快速发展和深度应用，为围绕学习者的学习场景和学习过程的全方位量化提供了可能，由此所产生的数据将有助于对学习者进行更深刻的全景式洞察，并为回答新一轮教育革命中的两大核心命题（科学化和精确化）提供了新途径。

学习的本质是学习主体知识自我构建和人格不断完善的过程，而这一过程需要依托一系列的学习场景和学习活动来完成。量化学习的主旨是助力于学习主体在学习场景和学习活动中高效地实现知识的建构乃至人格的完善：通过帮助施教者（如教师）更准确地把握学习者的状态，继而开展有针对性的教学活动；通过帮助学习者更深刻地洞察自身的问题，继而采取相应的行动来完善自己。从这个意义上来讲，量化学习是伴随着学习者的一个“知行合一”的过程，具有天然的实践性。作为探索信息技术与教育深度融合的重要抓手，量化学习将助力于科学化和精确化的“学习者中心化”学习新生态的构建。

在教育领域，“数据驱动学校、分析变革教育”的大数据时代已经来临，大数据掀起人类教与学的又一次变革。利用教育数据挖掘技术和学习分析技术，构建教育领域相关模型，探索教育变量之间的相关关系，为教育教学决策提供有效支持已经成为教育的发展趋势。

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