Ontology-based conceptual model for quality assurance in higher education

Quality in higher education is a complex, controversial and continuously evolving area of research. The concept of quality assurance (QA) emerged and is widely used nowadays within a range of processes of managing quality in higher education. A review of a number of existing standards of QA revealed many research gaps such as structure variations, lack of shared knowledge and understanding, lack of standardized use of terminology and the lack of practical and semantic support and guidelines on developing conceptual models of quality assurance in higher education. The Design Science (DS) approach in Information Systems discipline provides clear guidelines for designing, developing, demonstrating and evaluating novel solutions for defined problems with the aim of extending the boundaries of human and organizational capabilities by producing new, advanced and original artifacts. Therefore, to address the highlighted gaps, this research adopts the design science research methodology (DSRM) provided by Peffers (2008) comprising a sequence of six activities: (1) Problem Identification and Motivation, (2) Definition of the Objectives of a Solution, (3) Design and Development, (4) Demonstration, (5) Evaluation, and (6) Communication. This thesis demonstrates the applicability and usefulness of domain models with the phenomenon of quality in the higher education domain to support shared understanding, communication, and domain learning and problem-solving by introducing a universal approach to the domain of quality assurance. The ontology-based conceptual model for quality assurance (OntoQA), which is the main artifact delivered by this research, has been developed to faithfully capture the domain of quality assurance of academic programmes. OntoQA covers its domain to the extent required by intended usage, providing a reference ontology to facilitate design, development, monitoring, evaluation and improvement of quality academic programmes, and to assist in designing quality assurance systems. This research has introduced OntoQA as a new approach to designing, developing, monitoring and evaluating quality academic programmes, as well as the design and development of quality assurance systems. Quality assurance in higher education is a community-based process which requires consensus between stakeholders, therefore, OntoQA enhances communications, and facilitates streamlined collaboration on joint goals. Using OntoQA and getting familiar with the idea of conceptualising quality assurance in higher education facilitates tool developers, which would potentially help higher education providers to integrate quality when designing new programmes, or while reviewing and improving existing ones in conformance with international standards

Ontology Validation for Managers

Ontology driven conceptual modeling focuses on accurately representing a domain of interest, instead of making information fit an arbitrary set of constructs. It may be used for different purposes, like to achieve semantic interoperability (Nardi, Falbo and Almeida, 2013), development of knowledge representation models (Guizzardi and Zamborlini, 2012) and language evaluation (Santos, Almeida and Guizzardi,2010). Regardless its final application, a model must be accurately defined in order for it to be a successful solution. This new branch of conceptual modeling improves traditional techniques by taking into consideration ontological properties, such as rigidity, identity and dependence, which are derived from a foundational ontology. This increasing interest in more expressive languages for conceptual modeling is shown by OMGs request for language proposals for the Semantic Information Model Federation (SIMF) (OMG,2011). OntoUML (Guizzardi, 2005) is an example of a language designed for that purpose.Its metamodel (Carraretto, 2010) is designed to comply to the Unified Foundational Ontology (UFO). It focus on structural aspects of individuals and universals.Grounded on human cognition and linguistics, it aims to provide the most basic categories in which humans understand and classify things around them.In (Guizzardi, 2010) Guizzardi quotes the famous Dijkstras lecture, in which he discusses the humble programmer and makes an analogy entitled the humble ontologist. He argues that the task of ontology-driven conceptual modeling is extremely complex and thus, modelers should surround themselves with as many tools as possible to aid in the development of the ontology. These complexities arise from different sources. A couple of them come from foundational ontology itself, both its modal nature, which imposes modelers to deal with possibilities, and the many different restrictions of each ontological category. But they also come from the need of accurately defining instance level constraints, which require additional rules, outside of the languages graphical notation. To help modelers to develop high quality OntoUML models, a number of tools have been proposed to aid in different phases of conceptual modeling. From the construction of the models themselves using design patterns questions (Guizzardi et al., 2011), to automatic syntax verification (Benevides, 2010) and model validation through simulation (Benevides et al., 2010). The importance of domain specification that accurately captures the intended conceptualization has been recognized by both the traditional conceptual modeling community (Moody et al., 2003) and the ontology community (Vrandečić, 2009). In this research we want to improve (Benevides et al., 2010) initiative, but focus exclusively on the validation of ontology driven conceptual models, and not on verification. With the complexity of the modeling activity in mind, we want to help modelers to systematically produce high quality ontologies, improving precision and coverage (Gangemi et al., 2005) of the models. We intend to make the simulationbased approach available for users that are not experts in the formal method, relieving them of the need to learn yet another language, solely for the purpose of validating their models