Customizing Resources for NSDL

互联网的一个承诺是教育“大规模定制”。多亏了国家科学数字图书馆(nsdl；http://nsdl.org)，)，才有机会过滤数以百万计的资源，并为个别学习者定制它们。这一NSDL目标研究项目正在按照对教师和学习者重要的维度来组织数字图书馆--具体地说，认知特征(认知发展、空间和数学检索技能、阅读水平)、情感特征(自我效能、动机、对学科的信念/态度)和社会特征(性别、主要语言、种族)。研究人员假设，资源的定制将使访问者在NSDL中花费更多的时间，学生实现更深入的学习。作为试验床，研究人员正在数学论坛数字图书馆(MFDL；http://mathforum.org)，)内创建一个定制的学习环境“定制数学论坛”，该数字图书馆是最受欢迎的教学数字图书馆之一，拥有最大的用户社区(超过100万人)。研究人员正在根据认知、情感和社会维度对数字图书馆进行索引，并正在评估这种索引是否有助于利益相关者(教师、学生和贡献者/作者)找到有效和高效的材料，以及这种索引是否比随机选择资源时产生更有效的学习效果。项目活动包括：*为MFDL内的初中和高中教师和学生设计一个定制的学习环境；*创建一个门户网站，进入一个专门的图书馆，其中有750道算术和几何问题，针对特定的认知和行为技能进行个性化；*开发智能搜索工具和智能代理，在数字图书馆中搜索适当的资源；*在MFDL中整合一个增强的元数据系统，沿着对教师和教师重要的维度--例如，与国家教育标准以及认知、情感和社会特征的关系；*评估为学生和教师提供定制问题的影响；*将定制服务的工具分发给其他数字图书馆服务提供商。尽管用MFDL进行了描述，但这项研究是一般性的，其方法适用于许多NSDL藏品。该项目建立在三个领域的工具和技术之上：智能教学系统、数字图书馆和教学网络。这项研究直接涉及计算问题(分布式环境中机器代理的进步以及智能教师和数字图书馆的集成)以及认知和情感问题(人类学习特征和改善在线教学的学生模型)。这项研究应针对个体差异，特别是在代表性不足的少数群体和妇女中的差异，提供敏感的教学，并应揭示不同认知能力的学生通过不同教学形式学习的程度。

One promise of the Internet is educational "mass customization." Thanks to the National Science Digital Library (NSDL; http://nsdl.org), the opportunity exists to filter millions of resources and customize them for individual learners. This NSDL targeted research project is organizing digital libraries by dimensions that are important to teachers and learners--specifically, cognitive characteristics (cognitive development, spatial and math retrieval skills, reading level), affective characteristics (self-efficacy, motivation, beliefs/attitudes toward the subject), and social characteristics (gender, main language, ethnicity). The investigators hypothesize that customization of resources will result in visitors spending more time in NSDL and students achieving more in-depth learning.As a testbed, the investigators are creating a customized learning environment, "Customized MathForum," within the Math Forum Digital Library (MFDL; http://mathforum.org), which is one of the most popular instructional digital libraries and has one of the largest communities of users (over a million individuals). The investigators are indexing the digital library according to cognitive, affective, and social dimensions and are evaluating whether such indexing helps stakeholders (teachers, students, and contributors/authors) find effective and efficient material and whether such indexing results in more effective learning than when resources are chosen randomly. Project activities include:* designing a customized learning environment for middle school and high school teachers and students within MFDL;* generating a portal to a special library of 750 arithmetic and geometry problems individualized for specific cognitive and behavioral skills;* developing smart search tools and intelligent agents that will search the digital library for appropriate resources;* integrating an enhanced metadata system in MFDL along dimensions that are important to teachers and learners--e.g., relation to state educational standards, and cognitive, affective, and social characteristics;* evaluating the impact of providing customized problems for students and teachers; and* disseminating tools for customized services to other digital library service providers.Though described in terms of MFDL, this research is general and the methodology is applicable to many NSDL collections.This project builds on tools and technologies that have evolved from several NSF-supported projects in three domains: intelligent tutoring systems, digital libraries, and instructional networks. The research directly addresses computational issues (advances in machine agents in distributed environments and the integration of intelligent tutors and digital libraries) and cognitive and affective issues (human learning characteristics and student models that improve online instruction). The research should result in sensitive instruction that is responsive to individual differences, especially among underrepresented minorities and women, and should unveil the extent to which students of different cognitive abilities learn with different forms of teaching.