A Systematic Review of Process Modelling Methods and its Application for Personalised Adaptive Learning Systems

• Published in: Journal of international technology and information management 2018-07, Vol.27 (3), p.23-46
• Main Author: Okoye, Kingsley
• Format: Article
• Language: English
• Subjects: Adaptation; Adaptive systems; Attention deficit hyperactivity disorder; Behavior; Cognitive style; Computer science; Customization; Education; Information systems; Investigations; Knowledge bases (artificial intelligence); Learning; Literature reviews; Methods; Modelling; Online instruction; Ontology; Semantics; Studies; System effectiveness; Systematic review
• ISSN: 1543-5962; 1941-6679

This systematic review work investigates current literature and methods that are related to the application of process mining and modelling in real-time particularly as it concerns personalisation of learning systems, or yet still, e-content development. The work compares available studies based on the domain area of study, the scope of the study, methods used, and the scientific contribution of the papers and results. Consequently, the findings of the identified papers were systematically evaluated in order to point out potential confounding variables or flaws that might have been overlooked or missing in the current literature. In turn, a critical structured analysis of the studies was done in order to rate the value of the stated works and the outcomes. Theoretically, the results of the investigated papers were summarized and empirically represented, in order to help draw conclusions as well as provide recommendations for future researches. Indeed, the investigations and findings from the papers show that one of the key challenges in developing personalised adaptive intelligent systems for learning is to build an effectively represented users profile, learning styles or objects, and behaviours to help support reasoning about each learner. Perhaps, the resultant information systems need to be able to describe and support real world (i.e. semantic or metadata) interpretation about the different learners, and provide effective ways to adapt the information about each user based on the existing knowledge or data especially as it concerns references to and/or discovery of the different patterns that can be found within the knowledge-base.