Modelling Subject Domain Causality for Learning Content Renewal

Article type: Research Article

Authors: Gudas, Saulius^{1; *} | Tekutov, Jurij^{2; 3} | Butleris, Rimantas⁴ | Denisovas, Vitalijus²

Affiliations: [1] Institute of Data Science and Digital Technologies, Faculty of Mathematics and Informatics, Vilnius University, LT-04812 Vilnius, Lithuania | [2] Informatics and Statistics Department, Faculty of Marine Technologies and Natural Sciences, Klaipeda University, Bijunu St. 17, LT-91225 Klaipeda, Lithuania | [3] Information Technologies Department, Faculty of Technology, Klaipeda State University of Applied Sciences, Bijunu St. 10, LT-91223 Klaipeda, Lithuania | [4] Department of Information Systems, Kaunas University of Technology, LT-44249 Kaunas, Lithuania. E-mails: saulius.gudas@mii.vu.lt, jurij.tekutov@yahoo.com, rimantas.butleris@ktu.lt, vitalijus.denisovas@ku.lt

Correspondence: [*] Corresponding author.

Abstract: The paper deals with the causality driven modelling method applied for the domain deep knowledge elicitation. This method is suitable for discovering causal relationships in domains that are characterized by internal circular causality, e.g. control and management, regulatory processes, self-regulation and renewal. Such domains are organizational systems (i.e. enterprise) or cyber-social systems, also biological systems, ecological systems, and other complex systems. Subject domain may be of different nature: real-world activities or documented content. A causality driven approach is applied here for the learning content analysis and normalization of the knowledge structures. This method was used in the field of education, and a case study of learning content renewal is provided. The domain here is a real world area – a learning content is about. The paper is on how to align the existing learning content and current (new) knowledge of the domain using the same causality driven viewpoint and the described models (frameworks). Two levels of the domain causal modelling are obtained. The first level is the discovery of the causality of the domain using the Management Transaction (MT) framework. Secondly, a deep knowledge structure of MT is revealed through a more detailed framework called the Elementary Management Cycle (EMC). The algorithms for updating the LO content in two steps are presented. Traceability matrix indicates the mismatch of the LO content (old knowledge) and new domain knowledge. Classification of the content discrepancies and an example of the study program content analysis is presented. The main outcome of the causality driven modelling approach is the effectiveness of discovering the deep knowledge when the relevant domain causality frameworks are applicable.

Keywords: enterprise domain, causal knowledge, circular causality, domain causality, management transaction, learning content

Journal: Informatica, vol. 30, no. 3, pp. 455-480, 2019