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.
