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61.
多模态学习分析被认为是学习分析研究的新生长点,其中,多模态数据如何整合是推进学习分析研究的难点。利用系统文献综述及元分析方法,有助于为研究和实践领域提供全景式的关于多模态数据整合的方法与策略指导。通过对国内外363篇相关文献的系统分析发现:(1)多模态学习分析中的数据类型主要包含数字空间数据、物理空间数据、生理体征数据、心理测量数据和环境场景数据等5类。在技术支持的教与学环境中,高频、精细、微观的多模态学习数据变得可得、易得、准确。(2)多模态学习分析中的学习指标主要有行为、注意、认知、元认知、情感、协作、交互、投入、学习绩效和技能等。随着技术的发展和人们对学习过程的深刻洞察,学习指标也会变得更加精细化。(3)数据与指标之间展现出"一对一""一对多"和"多对一"三种对应关系。把握数据与指标之间的复杂关系是数据整合的前提,测量学习指标时既要考虑最适合的数据,也要考虑其他模态数据的补充。(4)多模态学习分析中的数据整合方式主要有"多对一""多对多"和"三角互证"三种,旨在提高测量的准确性、信息的全面性和整合的科学性。总之,多模态数据整合具有数据的多模态、指标的多维度和方法的多样性等三维特性。将多模态数据时间线对齐是实现数据整合的关键环节,综合考虑三维特性提高分析结果的准确性是多模态数据整合未来研究的方向。 相似文献
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In the last five decades, maturity models have been introduced as reference frameworks for Information System (IS) management in organizations within different industries. In the healthcare domain, maturity models have also been used to address a wide variety of challenges and the high demand for hospital IS (HIS) implementations. The increasing volume of data, is exceeded the ability of health organizations to process it for improving clinical and financial efficiencies and quality of care. It is believed that careful and attentive use of Data Analytics in healthcare can transform data into knowledge that can improve patient outcomes and operational efficiency. A maturity model in this conjuncture, is a way of identifying strengths and weaknesses of the HIS maturity and thus, find a way for improvement and evolution. This paper presents a proposal to measure Hospitals Information Systems maturity with regard to Data Analytics. The outcome of this paper is a maturity model, which includes six stages of HIS growth and maturity progression. 相似文献
63.
AI systems offer organizations great benefits causing decision-makers to invest more in these systems. The advantages of AI cannot be achieved without successful implementation. Thus, it is crucial to recognize the factors impacting the successful implementation of AI. It is also important to assess and rank these factors by their importance to assist decision-makers in implementing these systems and increasing the success rate. Due to its importance, scholars called for studies to expand our knowledge in this critical area. This paper identifies, extracts, and assesses the most critical factors that influence the implementation of AI systems. This study identifies nineteen factors and categorizes them into four categories: organization, technology, process, and environment. The analytical hierarchy process is used to evaluate the factors and the categories. The analysis offers two types of results, at the category level and the level of the factors. The results indicate that technology is the most significant of the four categories. The results also suggest that ethics is the most crucial factor among all nineteen factors. The order of all factors and discussions of the implications of the findings for practice and research are presented in the paper. 相似文献
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Universities and companies have decision-making processes that allow to achieve institutional objectives. Currently, data analysis has an important role in generating knowledge, obtaining important patterns and predictions for formulating strategies. This article presents the design of a business intelligence governance framework for the Universidad de la Costa, easily replicable in other institutions. For this purpose, a diagnosis was made to identify the level of maturity in analytics. From this baseline, a model was designed to strengthen organizational culture, infrastructure, data management, data analysis and governance. The proposal contemplates the definition of a governance framework, guiding principles, strategies, policies, processes, decision-making body and roles. Therefore, the framework is designed to implement effective controls that ensure the success of business intelligence projects, achieving an alignment of the objectives of the development plan with the analytical vision of the institution. 相似文献
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Tom Broos Isabel Hilliger Mar Pérez-Sanagustín Nyi-Nyi Htun Martijn Millecamp Paola Pesántez-Cabrera Lizandro Solano-Quinde Lorena Siguenza-Guzman Miguel Zuñiga-Prieto Katrien Verbert Tinne De Laet 《British journal of educational technology : journal of the Council for Educational Technology》2020,51(4):938-954
Many Latin-American institutions recognise the potential of learning analytics (LA). However, the number of actual LA implementations at scale remains limited, notwithstanding considerable effort made to formulate guidelines and frameworks to support the LA policy development. Guidance on how to coordinate the interaction between the LA policymaking and implementation is mostly missing, leaving a difficult challenge up to practitioners. In this study we propose a coordination model to support future LA initiatives at scale. We explore the problem by comparing two cases in Belgium and Ecuador. Following up we use the LA implementation timeline as a driver for planning the interaction between the policymaking and implementation. We continue by testing an application of the model with LA experts predominantly from Latin-American institutions, asking them to map low-level items of the SHEILA policy framework to four implementation phases. The results of this mapping support that LA policy building can be spread over time, that it can coincide with LA implementation at scale, and that both efforts can be coordinated. It is hoped that this study will provide additional guidance for future Latin-American and other LA initiatives. 相似文献
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Irene M. H. Herold 《Behavioral & Social Sciences Librarian》2013,32(4):267-282
Archival digital image collections are a relatively new phenomenon in college library archives. Digitizing archival image collections may make them accessible to users worldwide. There has been no study to explore whether collections on the Internet lead to users who are beyond the institution or a comparison of users to a national or international audience. This study of the Orang Asli Archive, a repository for anthropological, historical, journalistic, and other documentary sources relevant to Orang Asli peoples and cultures of Malaysia, examines the Web analytics of its digital archival image collections. 相似文献
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《Journal of Web Librarianship》2013,7(2-3):287-306
ABSTRACT Although there is a proliferation of information available on the Web, and law professors, students, and other users have a variety of channels to locate information and complete their research activities, the law library catalog still remains an important source for offering users access to information that has been evaluated and cataloged by experts. The usability of the catalog needs to be effectively measured before any necessary improvements can be made. This study was undertaken to investigate the information retrieval patterns of users of the Rutgers Law Library Online Public Access Catalog and to develop the catalog into a more effective search tool for these users. This study used an experimental approach to measure the usability of our catalog by analyzing the transaction logs from the OPAC system and the results from Google Analytics. The findings provided not only important information on user demographics and their computer systems, but also more insight on the search behaviors of users. The specific findings included the following:
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As a Web-analytic tool Google Analytics provided extensive information on the OPAC and the navigational behaviors of users.
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Fifty-eight percent of our users visited the Web site regularly.
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The most popular search method, which was employed by 37% of our users, was by title.
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Most patrons used computer systems with a high resolution and color depth monitor and visited the catalog Web site with a high-speed Internet connection.
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Suggestions were made by the authors to improve the users’ search experience of the catalog Web site.
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《Journal of Web Librarianship》2013,7(1):14-27
ABSTRACTDiscovery tools are used in libraries to bring together books, articles, and other resources. Research has focused on user and librarian evaluation of these tools, but there are few evaluations of non-book and non-article sources. Discovery tools can also include metadata for local collections harvested through the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH). Creating these harvests can be time consuming for staff, so it is important for libraries to understand if and how patrons use these records. The University of Nebraska-Lincoln Libraries (UNL Libraries) harvests metadata from local collections into the Encore discovery tool. A study was conducted to analyze patron use of OAI-harvested records. This study analyzed usage data for harvested collections obtained from different discovery sources and referrals through Encore. Google Analytics was used to evaluate searcher behavior differences between content referred through Encore and other referrals. Although discovery through Encore did not result in high numbers of traffic, there is evidence that patrons who discover records through Encore take more time looking through records than patrons using other discovery methods. This increase in time is a measure of engagement and may be reason enough for libraries to consider adding OAI-harvested collections to their discovery tool. 相似文献