The structural, functional, and production views on learning objects influence metadata structure and vocabulary. We drew on these views and conducted a literature review and in-depth analysis of 14 learning objects and over 500 components in these learning objects to model the knowledge framework for a learning object ontology. The learning object ontology reported in this paper consists of 8 top-level classes, 28 classes at the second level, and 34 at the third level. Except class Learning object, all other classes have the three properties of preferred term, related term, and synonym. To validate the ontology, we conducted a query log analysis that focused on discovering what terms users have used at both conceptual and word levels. The findings show that the main classes in the ontology are either conceptually or linguistically similar to the top terms in the query log data. We built an Exercise Editor as an informal experiment to test its ability to be adopted in authoring tools. The main contribution of this project is in the framework for the learning object domain and methodology used to develop and validate an ontology.

Metadata and an appropriate metadata model are nontrivial components of information architecture conceptualization and implementation, particularly when disparate and dispersed systems are integrated. Metadata availability can enhance retrieval processes, improve information organization and navigation, and support management of digital objects. To support these activities efficiently, metadata need to be modeled appropriately for the tasks. The authors' work focuses on how to understand and model metadata requirements to support the work of end users of an integrative statistical knowledge network (SKN). They report on a series of user studies. These studies provide an understanding of metadata elements necessary for a variety of user-oriented tasks, related business rules associated with the use of these elements, and their relationship to other perspectives on metadata model development. This work demonstrates the importance of the user perspective in this type of design activity and provides a set of strategies by which the results of user studies can be systematically utilized to support that design.

Many of the existing metadata standards use content metadata elements that are coarse-grained representations of learning resources. These metadata standards limit users' access to learning objects that may be at the component level. The authors discuss the need for component level access to learning resources and provide a conceptual framework of the knowledge representation of learning objects that would enable such access.

United States government services are becoming increasingly Web-based, creating opportunities to make useful, even vital, information and services more accessible to citizens than in the past. This opportunity has challenged Federal agencies as they work to provide information and services that are easy to use and understandable to an extremely diverse constituency. This paper reports the findings of a study examining the questions and uncertainties of users during investigation of statistical tables.The questions and uncertainties are categorized, mapped to an XML DTD for use in a table-browsing system. Implications of the approach and results are discussed. STUDY OVERVIEWAs the statistical community continues to disseminate its information electronically, it will become ever more critical for the metadata behind the data to be easily available for users and applications. The statistical agencies have been addressing these challenges via a variety of strategies and approaches.As illustrated by this research, enabling universal access and usability of statistical tables can be modeled as a process in which a user with an information need comes to a system in order to locate and then use a table or tables of interest. This paper reports on several specific technologies that were developed to support this process.This project addressed the following questions:• What questions and uncertainties do users have when investigating the statistical tables used in the NSF project? • What are the answers to these questions? • To what extent is metadata available to answer the questions? • How do the questions, question types, and answers map to the XML DTD developed by the NSF project to support the Table Browser? This study demonstrated that users have a variety of questions, some of may be easily resolved with available electronic documentation The preponderance of definitional questions had fairly easy resolutions, and in fact, definitions of variables, categories of variables, etc. are already well documented within existing metadata systems. This makes answers easy to retrieve.Some uncertainties are much more complex, however, in particular those relating to rationales. Answers to these questions seem to require a richer domain knowledge that might be difficult to retrieve. The categorization scheme developed in the project can serve to categorize questions in future studies in which the goal is to map to metadata sources and specify tool implementations.While users often have uncertainties that are highly contextual and related to their specific situation and experience, it is difficult to anticipate those in advance and provide previously encoded solutions. Finding the balance between completely contextualized and general answers needs further exploration. FUTURE RESEARCHThis work might be furthered with the following additional research:• Expand the identification and coding of user uncertainties to additional tables in order to further validate the coding scheme, potentially begin to determine relative frequencies ...

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