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），我们有机会过滤数百万资源，并为个别学习者定制它们。这个以NSDL为目标的研究项目是根据对教师和学习者重要的维度来组织数字图书馆，特别是认知特征（认知发展、空间和数学检索技能、阅读水平）、情感特征（自我效能感、动机、对学科的信仰/态度）和社会特征（性别、主要语言、种族）。研究人员假设，定制资源将导致访问者在非自然语言学习中花费更多的时间，学生获得更深入的学习。作为测试平台，研究人员正在数学论坛数字图书馆（MFDL; http://mathforum.org）中创建一个定制的学习环境，“定制的数学论坛”，这是最受欢迎的教学数字图书馆之一，拥有最大的用户社区之一（超过一百万个人）。研究者正在根据认知、情感和社会维度对数字图书馆进行索引，并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更有效地学习。项目活动包括：*为MFDL内的初高中师生设计定制化学习环境；*为特定的认知和行为技能生成一个包含750个算术和几何问题的特殊库的入口；*开发智能检索工具和智能代理，以便在数字图书馆中检索合适的资源；*在MFDL中集成一个增强的元数据系统，沿着对教师和学习者重要的维度，例如：与国家教育标准、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；*向其他数字图书馆服务提供商传播定制服务工具。虽然是用MFDL来描述的，但这项研究是通用的，其方法适用于许多NSDL集合。这个项目建立在工具和技术的基础上，这些工具和技术是从几个nsf支持的项目中发展而来的，这些项目涉及三个领域：智能辅导系统、数字图书馆和教学网络。该研究直接解决了计算问题（分布式环境中机器代理的进步以及智能导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）。这项研究应产生针对个体差异的敏感教学，特别是在代表性不足的少数民族和妇女之间，并应揭示不同认知能力的学生在不同形式的教学中学习的程度。

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.