16,928 research outputs found
Quality assurance for digital learning object repositories: issues for the metadata creation process
Metadata enables users to find the resources they require, therefore it is an important component of any digital learning object repository. Much work has already been done within the learning technology community to assure metadata quality, focused on the development of metadata standards, specifications and vocabularies and their implementation within repositories. The metadata creation process has thus far been largely overlooked. There has been an assumption that metadata creation will be straightforward and that where machines cannot generate metadata effectively, authors of learning materials will be the most appropriate metadata creators. However, repositories are reporting difficulties in obtaining good quality metadata from their contributors, and it is becoming apparent that the issue of metadata creation warrants attention. This paper surveys the growing body of evidence, including three UK-based case studies, scopes the issues surrounding human-generated metadata creation and identifies questions for further investigation. Collaborative creation of metadata by resource authors and metadata specialists, and the design of tools and processes, are emerging as key areas for deeper research. Research is also needed into how end users will search learning object repositories
The LSST Data Mining Research Agenda
We describe features of the LSST science database that are amenable to
scientific data mining, object classification, outlier identification, anomaly
detection, image quality assurance, and survey science validation. The data
mining research agenda includes: scalability (at petabytes scales) of existing
machine learning and data mining algorithms; development of grid-enabled
parallel data mining algorithms; designing a robust system for brokering
classifications from the LSST event pipeline (which may produce 10,000 or more
event alerts per night); multi-resolution methods for exploration of petascale
databases; indexing of multi-attribute multi-dimensional astronomical databases
(beyond spatial indexing) for rapid querying of petabyte databases; and more.Comment: 5 pages, Presented at the "Classification and Discovery in Large
Astronomical Surveys" meeting, Ringberg Castle, 14-17 October, 200
Geoscience after IT: Part L. Adjusting the emerging information system to new technology
Coherent development depends on following widely used standards that respect our vast legacy of existing entries in the geoscience record. Middleware ensures that we see a coherent view from our desktops of diverse sources of information. Developments specific to managing the written word, map content, and structured data come together in shared metadata linking topics and information types
Ontology technology for the development and deployment of learning technology systems - a survey
The World-Wide Web is undergoing dramatic changes at the moment. The Semantic Web is an initiative to bring meaning to the Web. The Semantic Web is based on ontology
technology – a knowledge representation framework – at its core. We illustrate the importance of this evolutionary development. We survey five scenarios demonstrating different forms of applications of ontology technologies in the development and deployment of learning technology
systems. Ontology technologies are highly useful to organise, personalise, and publish learning content and to discover, generate, and compose learning objects
Linking Data Across Universities: An Integrated Video Lectures Dataset
This paper presents our work and experience interlinking educational information across universities through the use of Linked Data principles and technologies. More specifically this paper is focused on selecting, extracting, structuring and interlinking information of video lectures produced by 27 different educational institutions. For this purpose, selected information from several websites and YouTube channels have been scraped and structured according to well-known vocabularies, like FOAF 1, or the W3C Ontology for Media Resources 2. To integrate this information, the extracted videos have been categorized under a common classification space, the taxonomy defined by the Open Directory Project 3. An evaluation of this categorization process has been conducted obtaining a 98% degree of coverage and 89% degree of correctness. As a result of this process a new Linked Data dataset has been released containing more than 14,000 video lectures from 27 different institutions and categorized under a common classification scheme
Graph-RAT: Combining data sources in music recommendation systems
The complexity of music recommendation systems has increased rapidly in recent years, drawing upon different sources of information: content analysis, web-mining, social tagging, etc. Unfortunately, the tools to scientifically evaluate such integrated systems are not readily available; nor are the base algorithms available. This article describes Graph-RAT (Graph-based Relational Analysis Toolkit), an open source toolkit that provides a framework for developing and evaluating novel hybrid systems. While this toolkit is designed for music recommendation, it has applications outside its discipline as well. An experiment—indicative of the sort of procedure that can be configured using the toolkit—is provided to illustrate its usefulness
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