Development of an ontology of course design in higher education

Ensuring quality is paramount in course design and delivery within higher education tertiary institutions. Individuals involved in the design and delivery of courses or programs in these institutions endeavour to ensure that delivered courses meet the highest standards. Quality assurance and compliance with higher education standards is crucial. Despite the significance of quality assurance, there is no standardized course design ontology in national and international lists and literature. This research seeks to fill this gap by developing an ontology tailored specifically for course design in higher education. Specifically, this research first identifies the course objects and their properties or attributes, characterises their relationships, and then organises them into a coherent ontology framework. An object in the course design ontology is a data field with unique attributes. Utilizing Classic Grounded Theory Methodology (CGTM), this study examines metadata associated with course design towards the development of a theoretically based ontology, based on the observed data patterns.
Three primary questions guide this investigation. First, this study addresses the challenges posed by disparate terminology across the higher education sector, identifying common concepts and terms essential for achieving consensus among stakeholders. Little information about course design processes is available although tools have been developed to manually record course and subject information. In both the tools and the literature, the lack of commonalities of terms for the same objects must be noted, as these present issues in understanding between all stakeholders.
To tackle these challenges, this study conducts a comprehensive analysis of the Higher Education Standards Framework (2015 & 2021), 14 Guidance Notes to the Framework (TEQSA, 2017, 2018, 2019), and 328 relevant policy documents from 32 higher education institutions, as well as literature on the education and computing disciplines encompassing course, curriculum and assessment design. After determining common meanings of terms and their interchangeability, key terms across legislation, Guidance Notes and policy were confirmed. The confusion around the use of different terminologies underscores the need for common understanding. Second, a theoretical base for automated mapping has been laid through the organisation of course objects. In the context of this study, automated mapping means the identification, collection and display of information gathered by a computer in graphical and textual format to demonstrate the instances and occurrences of learning objects across a course. Identifying the metadata of objects enables cross linking of the subjects within courses and across multiple courses where applicable. This has been achieved by using available software tools. The emerging pattern across a course using mapping can assist in ensuring that misalignment does not occur. The ontology is illustrated using various diagrams, together with explanations. Third, the proposed ontology has been evaluated and illustrated using a case study of a qualification with 24 core subjects. The ontology identifies relationships between the objects of the course, so the patterns formed by mapping the metadata contained in the course design ontology objects demonstrate links between outcomes, content and assessment.
Further research into the achievement of specific learning outcomes could further clarify relationships between assessment objects in the ontology. Graduate attribute ontologies were another area ripe for development that emerged along with an expansion of academic analytics focusing on course data feeding into course design processes. The development of software or applications that can draw from academic analytics and be flexible in its use of terminology to suit each institution would be the next step to build on the knowledge developed in this thesis. As artificial intelligence (AI) capacity improves, it can assist educators to design and maintain courses more efficiently, ensuring quality assurance can be more readily achieved as errors and misalignment become visible though its use. AI and educational data mining can be used to find, map and analyse course data automatically which will influence decision makers at all levels and support quality in education, freeing up academic and designers’ time to build better courses.
This research has contributed a validated ontology for automated mapping of course design information, offering a feasible framework for use in course design mapping and improving quality of course design processes in higher education institutions.