Engineering Materials and Personal Spaces in Public Repositories: The Case of the MERLOT Digital Library

The open educational resources are learning, teaching and research materials available in open sites for free access, and are frequently grouped into personal collections by users. MERLOT, the well-known online repository, includes amongst its materials Bookmark Collections (BC) created by its contributors. This article evaluates, from the point of view of users other than the creators, the usefulness of the MERLOT personal collections in engineering education. There are at least 895 Bookmark Collections in the Engineering collection of MERLOT. The main assessment criteria are the potential of shareability and reusability of the collection in terms of the engineering content and description, and the coherence of materials and collection with the respective engineering discipline and sub-discipline. Results show that the quality of these collections could be improved if the assignment of the collection would be declared, as the title and description, during the BC creation. The improvement of the BC title and most especially its description would be also a good improvement that will guide the searcher more precisely

Automatically evaluating the quality of textual descriptions in cultural heritage records

AbstractMetadata are fundamental for the indexing, browsing and retrieval of cultural heritage resources in repositories, digital libraries and catalogues. In order to be effectively exploited, metadata information has to meet some quality standards, typically defined in the collection usage guidelines. As manually checking the quality of metadata in a repository may not be affordable, especially in large collections, in this paper we specifically address the problem of automatically assessing the quality of metadata, focusing in particular on textual descriptions of cultural heritage items. We describe a novel approach based on machine learning that tackles this problem by framing it as a binary text classification task aimed at evaluating the accuracy of textual descriptions. We report our assessment of different classifiers using a new dataset that we developed, containing more than 100K descriptions. The dataset was extracted from different collections and domains from the Italian digital library "Cultura Italia" and was annotated with accuracy information in terms of compliance with the cataloguing guidelines. The results empirically confirm that our proposed approach can effectively support curators (F1 $$\sim$$ ∼ 0.85) in assessing the quality of the textual descriptions of the records in their collections and provide some insights into how training data, specifically their size and domain, can affect classification performance

Automatically evaluating the quality of textual descriptions in cultural heritage records

Metadata are fundamental for the indexing, browsing and retrieval of cultural heritage resources in repositories, digital libraries and catalogues. In order to be effectively exploited, metadata information has to meet some quality standards, typically defined in the collection usage guidelines. As manually checking the quality of metadata in a repository may not be affordable, especially in large collections, in this paper we specifically address the problem of automatically assessing the quality of metadata, focusing in particular on textual descriptions of cultural heritage items. We describe a novel approach based on machine learning that tackles this problem by framing it as a binary text classification task aimed at evaluating the accuracy of textual descriptions. We report our assessment of different classifiers using a new dataset that we developed, containing more than 100K descriptions. The dataset was extracted from different collections and domains from the Italian digital library \u201cCultura Italia\u201d and was annotated with accuracy information in terms of compliance with the cataloguing guidelines. The results empirically confirm that our proposed approach can effectively support curators (F1 3c 0.85) in assessing the quality of the textual descriptions of the records in their collections and provide some insights into how training data, specifically their size and domain, can affect classification performance

Using Your Library’s Objectives as the Organizational Framework for Library Documentation in Planning, Assessment, and Accreditation

The San Diego Christian/Southern California Seminary Library1 uses its objectives as the organizational framework for its strategic plan, annual report, assessment plan, and policies and procedures manual. This article describes how the library’s objectives compare to the Association of College and Research Libraries’ Standards (to ensure best practices), relate to the areas covered in the library strategic plan, annual report, and operations manual, and correspond to the standards and criteria from their respective accrediting agencies, showing how easy it is to identify supporting evidence for a program review or self-study when using this organizing method

ULS FY14 Planning and Budget Report

This document was submitted by the University Library System (ULS) to the University of Pittsburgh's provost's office on March 1, 2013. Incorporating the work of the ULS FY14 Planning Task Force, it reports ULS accomplishments for 2012-2013 and strategic priorities for 2013-2014

DeWitt Wallace Library Annual Report 2013-2014

Summary of library and media services activities for 2013-201

Bridging the gap between digital libraries and e-learning

Digital Libraries (DL) are offering access to a vast amount of digital content, relevant to practically all domains of human knowledge, which makes it suitable to enhance teaching and learning. Based on a systematic literature review, this article provides an overview and a gap analysis of educational use of DLs.The research work presented in this paper is partially supported by the FP7 Grant 316087 AComIn ”Advanced Computing for Innovation”, funded by the European Commission in the FP7 Capacity Programme in 2012-2016.peer-reviewe