How do we acquire understanding of conceptual models?

In organizations, conceptual models are used for understanding the domain concepts. Such models are crucial in analysis and development of information systems. An important factor of using the conceptual models is how quickly analysts are able to learn the domain concepts as depicted in the models. Using a laboratory experiment, this research used eye tracking technique to capture the speed of acquisition of understanding conceptual models. Two sets of conceptual models were used in this study- one theory based (REA pattern) and the other non-theory based (non REA pattern). It was found that the rate of learning of the domain concepts was faster with theory based models than with non-theory based models. However, users of the non-theory based model were able to catch up with the learning of the model concepts after being repeatedly exposed to the model

A Cognitive Perspective on How Experts Develop Conceptual Models in Complex Domains

Conceptual models are important in understanding the domain which is to be reflected in the information systems. Development of such models involves experts in conceptual modeling techniques (ISDK experts) and experts in the domain application (ISAK experts). This paper focuses on understanding how these two types of experts interact and develop conceptual models jointly. Using an exploratory study, it was identified that in the early phase of development of conceptual models, the experts focus on understanding concepts of the domains that they are not familiar with. Later, when the experts had shared information on the concepts of the domains then they focus on developing the conceptual model. The study also indicates that the groups of experts that have high shared information are most likely to create high quality conceptual models

A FRAMEWORK TO CLARIFY THE ROLE OF KNOWLEDGE MANAGEMENT SYSTEMS

Knowledge management Systems (KMS) are IT applications that manage representations of organizational knowledge. This paper presents a conceptual model of KMS which adapts an artificial intelligence (AI) based view of knowledge. According to this view, knowledge can be defined in terms of agent, action, state, and goal. The conceptual model is intended to help differentiate the role of KMS from that of Information Systems (IS). The knowledge managed by the KMS is intended to enable an agent to choose actions that can be taken to accomplish a goal in the given state. The role of the IS is to make the agent aware of a situation described in terms of states, actions, and goal. The model suggests that whether we classify an IT application as KMS or IS depends on the contents it manages and to increase the effectiveness of KMS, it is often necessary to use IS that complement the KMS. Considering the importance and popularity of KMS in organizations, we believe the clarification of the role of KMS is useful

How quickly do we learn conceptual models?

In organizations, conceptual models are used for understanding domain concepts. Learning the domain from models is crucial for the analysis and design of information systems that are intended to support the domain. Past research has proposed theories to structure conceptual models in order to improve learning. It has, however, never been investigated how quickly domain knowledge is acquired when using theory-guided conceptual models. Based on theoretical arguments, we hypothesize that theory-guided conceptual models expedite the initial stages of learning. Using the REA ontology pattern as an example of theoretical guidance, we show in a laboratory experiment how an eye-tracking procedure can be used to investigate the effect of using theory-guided models on the speed of learning. Whereas our experiment shows positive effects on both outcome and speed of learning in the initial stages of learning, the real contribution of our paper is methodological, i.e. an eye-tracking procedure to observe the process of learning from conceptual models

Special Theme of Research in Information Systems Analysis and Design - I. Unraveling Knowledge Requirements Through Business Process Analysis

Organizations analyze their business processes in order to improve them. Business processes are also considered retainers, users and creators of organizational knowledge. Thus, they can be analyzed to identify the knowledge used, created and embedded in them. A process analysis approach that focuses on redesign does not necessarily capture the knowledge used and created in a process. Choosing a knowledge-focused approach should lead to understanding knowledge needs but might not lead to improved business processes. This paper describes an approach for Knowledge Requirements Analysis (KRA) that combines process analysis with identifying knowledge used and created during the process. KRA is the process of identifying and analyzing existing organizational knowledge and prescribing improvements to it. The KRA methodology presented in this paper combines two methods: a knowledge engineering method (CommonKADS) and a process modeling method (EDPDT). The EDPDT constructs are used to operationalize the organization and task models of CommonKADS and thus create the KRA methodology. The methodology was applied successfully to the process of ethical reviews of grant applications in a university. The main advantage of the proposed methodology is that it enables organizations to keep track of their knowledge resources embedded in various business processes. Knowledge that is not shared or used can be detected and new knowledge can be identified to support and improve existing processes better. This approach can lead to improved knowledge management in organization

A Research Agenda on Using Conceptual Models for User Story Development

Agile practitioners and researchers have identified many challenges related to the requirements in agile projects. Some of these challenges relate to documentation and more specifically the development, maintenance, and management of user stories. This research addresses some of the user stories challenges by proposing the use of conceptual models while developing user stories. Conceptual models are presentations used for domain understanding. The proposal considers development of such conceptual models automatically while user stories are developed. A detailed research plan has been developed to conduct this research

THE EFFECT OF DOMAIN FAMILIARITY ON MODELLING ROLES: AN EMPIRICAL STUDY

Conceptual modelling (CM) involves analysts working with domain experts to create a representation of the domain called a conceptual model. We address two issues of CM research. The first deals with the meaning that conceptual models convey. We propose guidelines for how analysts can reflect the concept of a “role” in a conceptual model using the extended entity relationship (EER) method. Roles are important in organizations, but analysts have little guidance about how to model them. The second issue focuses on the effect of prior domain familiarity of the users on the understanding of conceptual models. We conducted a laboratory study to determine how domain familiarity affects users’ understanding of conceptual models that represent roles. Our results indicate that conceptual models can be developed to show roles more clearly but that the benefit of doing so depends on model readers’ familiarity with the modeled domain. In particular, our guidelines will be most useful when users have moderate knowledge of the domain shown in the model. When users are very familiar with the domain, the guidelines do not seem to have much benefit. However, when users have very little knowledge of the domain, the guidelines do help to a certain extent

Challenging the Artifacts and Practices Adopted in Agile Software Development

Roughly speaking, agile software development methods include the adoption of an iterative life cycle and of special kinds of artifacts and practices. The life cycle is the result of years of improvement in software development starting from waterfall, going through planned long iterations like in the Rational Unified Process, to finally end-up with short, unplanned sprints. The used artifacts and practices nevertheless deserve more research in order to measure development performance, to analyze their optimal uses, and to determine the opportunity of their integration in a custom agile process. This paper highlights the need for challenging the artifacts and practices from a scientific standpoint. While doing so, we briefly discusses the research conducted in the field user stories–that have often been studied in the literature in combination with conceptual modeling–, before outlining the first edition of the Agil-ISE workshop and discussing future directions