Ontology-Based Smart System to Automate Higher Education Activities. (3rd August 2021)

• Record Type: Journal Article
• Authors: Alrehaili, Nada Abdullah; Aslam, Muhammad Ahtisham; Alahmadi, Dimah Hussein; Alrehaili, Dina Abdullah; Asif, Muhammad; Arshad Malik, Muhammad Sheraz
• Other Names: Ahmad Muhammad Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-08-03
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/5588381
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : The need for smart e-learning environments is resulting in new challenges for researchers and practitioners to develop intelligent systems that can be used to automate the Higher Education (HE) activities in an intelligent way. Some common examples of such activities are "analyzing, finding, and ranking the right resource to teach a course, " "analyzing and finding the people with common research interests to start joint research projects, " and "using data analytics and machine reasoning techniques for conducting the exams with different levels of complexities." Ontological reasoning and smart data analytics can play an important role in analyzing and automating these HE activities and processes. In this paper, we present a framework named as Higher Education Activities and Processes Automation Framework (HEAPAF). The HEAPAF framework can be used to identify, extract, process, and produce the semantically enriched data in machine understandable format from different educational resources. We also present the Higher Education Ontology (HEO) that we designed and developed to accommodate the HE data and then to perform analysis and reasoning on it. As a proof of concept, we present a case study on the topic, "analyzing, finding, and ranking the right resources to teach a course, " which can dramatically improve the learning patterns of students in the growing smart educational environment. Finally, we provide the evaluation of our framework as evidence of its competency and consistency in improving academic analytics for educational activities and processes by using machine reasoning.