Search (6 results, page 1 of 1)

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Abstract

Educational standards are a central focus of the current educational system in the United States, underpinning educational practice, curriculum design, teacher professional development, and high-stakes testing and assessment. Digital library users have requested that this information be accessible in association with digital learning resources to support teaching and learning as well as accountability requirements. Providing this information is complex because of the variability and number of standards documents in use at the national, state, and local level. This article describes a cataloging tool that aids catalogers in the assignment of standards metadata to digital library resources, using natural language processing techniques. The research explores whether the standards suggestor service would suggest the same standards as a human, whether relevant standards are ranked appropriately in the result set, and whether the relevance of the suggested assignments improve when, in addition to resource content, metadata is included in the query to the cataloging tool. The article also discusses how this service might streamline the cataloging workflow.

Date

22. 1.2011 14:25:32

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.395-405

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Source

Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly

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Abstract

In this study, we design an innovative method for answering students' or scholars' academic questions (for a specific scientific publication) by automatically recommending e-learning resources in a cyber-infrastructure-enabled learning environment to enhance the learning experiences of students and scholars. By using information retrieval and metasearch methodologies, different types of referential metadata (related Wikipedia pages, data sets, source code, video lectures, presentation slides, and online tutorials) for an assortment of publications and scientific topics will be automatically retrieved, associated, and ranked (via the language model and the inference network model) to provide easily understandable cyberlearning resources to answer students' questions. We also designed an experimental system to automatically answer students' questions for a specific academic publication and then evaluated the quality of the answers (the recommended resources) using mean reciprocal rank and normalized discounted cumulative gain. After examining preliminary evaluation results and student feedback, we found that cyberlearning resources can provide high-quality and straightforward answers for students' and scholars' questions concerning the content of academic publications.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.8, S.1707-1722

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Abstract

Along with significant opportunities, Massively Open Online Courses (MOOCs) provide major challenges to students (keeping track of course materials and effectively interacting with teachers and fellow students), teachers (managing thousands of students and supporting their learning progress), researchers (understanding how students interact with materials and each other), and MOOC platform developers (supporting effective course design and delivery in a scalable way). This article demonstrates the use of data analysis and visualization as a means to empower students, teachers, researchers, and platform developers by making large volumes of data easy to understand. First, we introduce the insight needs of different stakeholder groups. Second, we compare the wide variety of data provided by major MOOC platforms. Third, we present a novel framework that distinguishes visualizations by the type of questions they answer. We then review the state of the art MOOC visual analytics using a tabulation of stakeholder needs versus visual analytics workflow types. Finally, we present new data analysis and visualization workflows for statistical, geospatial, and topical insights. The workflows have been optimized and validated in the Information Visualization MOOC (IVMOOC) annually taught at Indiana University since 2013. All workflows, sample data, and visualizations are provided at http://cns.iu.edu/2016-MOOCVis.html.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.10, S.2350-2363

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Source

Journal of the American Society for Information Science and Technology. 62(2011) no.11, S.2182-2200

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Source

Journal of the Association for Information Science and Technology. 70(2019) no.2, S.140-150