移动学习中片段式学习资源的设计研究
张驰1 陈刚1 王敏娟2 王慧敏1
（1.上海交通大学现代远程教育研究发展中心，上海200030；
2. 圣地亚哥州立大学 教育技术系，美国）
【摘要】随着网络技术与通信技术的迅速发展，移动学习呈现出强大的发展势头，它真正为学习者的随时随地学习提供了可能性，但学习者在移动学习过程中更容易受到外界干扰，而且学习者个性化的学习需求给移动学习资源设计提出了更高的要求，这成为移动学习设计研究中需要解决的主要问题之一。本研究基于课程知识点进行片段式学习资源的设计与开发，试验性地应用于移动学习中，并在学习者完成一个阶段的移动学习后，利用网上问卷调查的形式收集学习者的反馈信息，共得到940份有效问卷。研究还引入数据挖掘技术，对大规模数据进行聚类分析，得到了较理想的聚类结果，进而从各类型学习者的特征属性和类间的变化规律出发，分析研究片段式学习资源在移动学习中的可行性和具体设计策略。
【关键词】移动学习；片段式；资源设计；知识点；聚类分析
【中图分类号】G434
【文献标识码】A
【文章编号】1007-2179（2009）03-0067-06
【作者简介】张驰，在读硕士，上海交通大学现代远程教育研究发展中心（zciversonhunter@gmail.com）；陈刚，博士，高级工程师，上海交通大学现代远程教育研究发展中心；王敏娟，博士，副教授，美国圣地亚哥州立大学教育技术系；王慧敏，在读硕士，上海交通大学现代远程教育研究发展中心。
Studying on Design of Small-chunk Learning
Resources in Mobile Learning
ZHANG Chi1,CHEN Gang1,WANG Minjuan2 &WANG Huimin1
(1. E-Learning Lab, Shanghai Jiao Tong University, Shanghai 200030, China;
2. Department Educational Technology, San Diego State University, United States)
Abstract: With the rapid development of network technology and communication technology, mobile-learning is presenting a strong developing trend. It indeed provides learners with possibilities of anytime-anywhere learning. But learners suffer more interruptions from surrounding environment and their need on individual learning makes a higher demand on design of mobile learning resources, which is one of the key problems in mobile-learning design. This article reports our efforts in designing and developing "small-chunk" learning resources that are based on knowledge points and experimentally applied in mobile-learning. After learners finish one stage of mobile-learning by using these learning materials, we collect survey data from 940 participants on web. In addition, this research applied data-ming technology to execute a cluster analysis on large-scale data which produced a more valid result. By defining characteristics of various style learners and changing rule among them, research analyzes the feasibility and detailed design strategy of small-chunk self-contained learning sources in mobile-learning.
Key Words: mobile learning; small-chunk; resources design; knowledge points; cluster analysis
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