Ontology Development for Creating Identical Software Environments to Improve Learning Outcomes in Higher Education Institutions

Abstract

Students engage in remote learning within a diverse computer environment. While virtual machines can address the challenges posed by heterogeneity, there remain unresolved issues, particularly related to the complexity of software management. An imperative is to discover an automated solution that facilitates the creation of consistent software environments for educational purposes. This paper introduces ontology engineering principles as a means to tackle the complexities associated with software management. A suitable ontology is developed using OWL syntax, integrating knowledge pertaining to the required software within a specific academic domain. The practical applicability of this knowledge is enabled through the implementation of dedicated SPARQL queries within a Python program. The effectiveness of the automated solution in achieving identical software environments is verified through testing, conducted in both controlled laboratory settings and by students themselves, thus simulating authentic teaching scenarios. The solution not only adheres to the principles of reusability but can also be adapted or integrated into existing ontologies. Furthermore, it presents an opportunity to create automated and self-adjusting virtual machines, offering significant potential for educational and other domains.

Ontological approach for competency-based curriculum analysis

“…In pursuit of answers to the aforementioned research questions, the authors aimed to develop an efficient competency model that would harness the full advantages of the competency-based approach [ [46] , [47] , [48] , [49] , [50] , [51] , 52 ] in educational program design. While addressing this objective, the authors aspired to create a descriptive framework for educational program competencies that would be expressive, transparent, and amenable to computer-based processing and analysis.…”

Interoperability-Enhanced Knowledge Management in Law Enforcement: An Integrated Data-Driven Forensic Ontological Approach to Crime Scene Analysis

“…Unlike relational databases that struggle with semantic ambiguities, the ontology approach captures complex relationships and enables intricate queries. Moreover, while keywordbased searches offer a simpler method for data retrieval, they fall short in creating semantic connections between data entities [85][86][87][88]. Rule-based expert systems, although effective for specific tasks, lack the flexibility to adapt to the diverse and dynamic nature of crime scene investigations.…”

“…Protege, a free, open-source ontology editor developed by the Stanford Center for Biomedical Informatics Research, is a critical tool widely employed for this purpose. Within this software environment, the creation, manipulation, and sharing of ontologies are facilitated, offering researchers a robust platform for academic and industrial applications [76,88,119,120].…”

“…Object properties specify how instances of one type can be related to models of another, thus capturing the complexity and interconnectedness of domain-specific entities. For example, an object property could specify that a "Patient" is related to a "Disease" through a "hasDiagnosis" relationship, enabling the formalization of intricate domain knowledge [88,119].…”

“…Unlike object properties, which link cases of different types, data properties connect models to data values such as strings, numbers, or dates. In a healthcare ontology, for example, a data property might specify that a "Patient" has an "age" that is an integer or a "name" that is a string [88,119].…”