Design and Implementation of Knowledge Base for Runtime Management of Software Deﬁned Hardware

Within the field of Digital Humanities, a great effort has been made to digitize documents and collections in order to build catalogs and exhibitions on the Web. In this paper, we present WeME, a Web application for building a knowledge base, which can be used to describe digital documents. WeME can be used by different categories of users: archivists/librarians and scholars. WeME extracts information from some well-known Linked Data nodes, i.e. DBpedia and GeoNames, as well as traditional Web sources, i.e. VIAF. As a use case of WeME, we describe the knowledge base related to the Christopher Clavius’s corre spondence. Clavius was a mathematician and an astronomer of the XVI Century. He wrote more than 300 letters, most of which are owned by the Historical Archives of the Pontifical Gregorian University (APUG) in Rome. The built knowledge base contains 139 links to DBpedia, 83 links to GeoNames and 129 links to VIAF. In order to test the usability of WeME, we invited 26 users to test the application.

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Design and Implementation of a Knowledge Base for Machine Knowledge Learning

2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) ◽

10.1109/icci-cc.2018.8482034 ◽

2018 ◽

Cited By ~ 2

Author(s):

Yingxu Wang ◽

Omar A. Zatarain

Keyword(s):

Knowledge Base ◽

Design And Implementation ◽

Knowledge Learning

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Unpacking the ‘Learning' in Student-Facing Analytics

International Journal of Adult Vocational Education and Technology ◽

10.4018/ijavet.2019010101 ◽

2019 ◽

Vol 10 (1) ◽

pp. 1-12

Author(s):

Joy Galaige ◽

Geraldine Torrisi-Steele

Keyword(s):

University Students ◽

Knowledge Base ◽

Learning Analytics ◽

Zone Of Proximal Development ◽

Early Research ◽

Learning Processes ◽

Proximal Development ◽

Learning And Teaching ◽

Effective Learning ◽

Design And Implementation

Founded on the need to help university students develop a greater academic metacognitive capacity, student-facing learning analytics are considered useful tools for making students overtly aware of their own learning processes, helping students to develop control over their learning, and subsequently supporting more effective learning. However, early research on the effectiveness of student-facing analytics is giving mixed results and is casting some doubt over the usefulness of student-facing learning analytics. One factor contributing to doubt over the value of student-facing learning analytics is that their design and implementation remains firmly rooted in the technical domain, with virtually no grounding in the knowledge base of learning and teaching. If the growing investment of resources into the development of student-facing learning analytics systems is to be fruitful, then there is an obvious, urgent need to re-position student-facing learning analytics within learning and teaching frameworks. With this in mind, we use Schraw & Dennison's model of metacognition and Vygotsky's zone of proximal development to unpack the ‘learning' in student-facing analytics and work towards an understanding of student-facing analytics that is more conducive to supporting metacognition and effective learning.

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