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NreASAM: Towards an Ontology-Based Model for Authentication and Auto-Grading Online Submission of Psychomotor Assessments
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Author(s): A. Kayode Adesemowo (Nelson Mandela University, South Africa)and Oluwasefunmi 'Tale Arogundade (Federal University of Agriculture, Abeokuta, Nigeria)
Copyright: 2021
Pages: 24
Source title:
Advanced Concepts, Methods, and Applications in Semantic Computing
Source Author(s)/Editor(s): Olawande Daramola (Cape Peninsula University of Technology, South Africa)and Thomas Moser (St. Pölten University of Applied Sciences, Austria)
DOI: 10.4018/978-1-7998-6697-8.ch009
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Abstract
Core and integral to the fourth industrial revolution, knowledge economy, and beyond is information and communication technology (ICT); more so, during and post the novel coronavirus pandemic. Yet, there exists a skills gap in ICT networking and networks engineering. Not only do students perceive ICT networking to be difficult to comprehend, lecturers and institutions grapple with the adequacy of ICT networking equipment. Real-life simulators, like the Cisco Packet Tracer, hold the promise of alternate teaching opportunities and evidenced-based environments for (higher-order) assessment. Research in the last decade on ontology for assessments have focused on taxonomy and multiple-choice questions and auto-generation and marking of assessments. This chapter extends the body of knowledge through its ontology-based model for enabling and auto-assessing performance-based and/or pseudo-psychomotor assessment. The auto-grading online submission system assists with authenticity and enables authentic and/or sustainable assessments.
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