Supporting contextualized learning with linked open data

IF 2.1 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Adolfo Ruiz-Calleja, Guillermo Vega-Gorgojo, Miguel L. Bote-Lorenzo, Juan I. Asensio-Pérez, Yannis Dimitriadis, Eduardo Gómez-Sánchez
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引用次数: 5

Abstract

This paper proposes a template-based approach to semi-automatically create contextualized learning tasks out of several sources from the Web of Data. The contextualization of learning tasks opens the possibility of bridging formal learning that happens in a classroom, and informal learning that happens in other physical spaces, such as squares or historical buildings. The tasks created cover different cognitive levels and are contextualized by their location and the topics covered. We applied this approach to the domain of History of Art in the Spanish region of Castile and Leon. We gathered data from DBpedia, Wikidata and the Open Data published by the regional government and we applied 32 templates to obtain 16K learning tasks. An evaluation with 8 teachers shows that teachers would accept their students to carry out the tasks generated. Teachers also considered that the 85% of the tasks generated are aligned with the content taught in the classroom and were found to be relevant to learn in other informal spaces. The tasks created are available at https://casuallearn.gsic.uva.es/sparql.

通过关联的开放数据支持情境化学习
本文提出了一种基于模板的方法,从Web of Data的多个数据源中半自动地创建情境化学习任务。学习任务的情境化为在教室中进行的正式学习和在其他物理空间(如广场或历史建筑)中进行的非正式学习之间架起了桥梁。所创建的任务涵盖不同的认知水平,并根据其位置和所涵盖的主题进行上下文化。我们将这种方法应用于西班牙卡斯蒂利亚和莱昂地区的艺术史领域。我们从DBpedia, Wikidata和地区政府发布的开放数据中收集数据,并应用32个模板获得16K个学习任务。对8名教师的评估表明,教师会接受学生执行生成的任务。教师们还认为,生成的任务中有85%与课堂教学内容一致,并且发现与其他非正式空间的学习相关。创建的任务可在https://casuallearn.gsic.uva.es/sparql上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Web Semantics
Journal of Web Semantics 工程技术-计算机:人工智能
CiteScore
6.20
自引率
12.00%
发文量
22
审稿时长
14.6 weeks
期刊介绍: The Journal of Web Semantics is an interdisciplinary journal based on research and applications of various subject areas that contribute to the development of a knowledge-intensive and intelligent service Web. These areas include: knowledge technologies, ontology, agents, databases and the semantic grid, obviously disciplines like information retrieval, language technology, human-computer interaction and knowledge discovery are of major relevance as well. All aspects of the Semantic Web development are covered. The publication of large-scale experiments and their analysis is also encouraged to clearly illustrate scenarios and methods that introduce semantics into existing Web interfaces, contents and services. The journal emphasizes the publication of papers that combine theories, methods and experiments from different subject areas in order to deliver innovative semantic methods and applications.
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