Stijn Hoppenbrouwers

Cognitive Mechanisms of Conceptual Modelling

Conceptual modelling involves many higher order cognitive processes, such as relational reasoning and abstraction, which are based on integration and maintenance of information. Evidence from cognitive psychology suggests that these processes are subject to individual differences which cannot be explained by training and experience alone. In this review, we study how the cognitive processes that enable modelling interact to produce modelling behaviour, and where in this process we can find individual differences that may explain some of the variation in performance seen in actual modelling settings. We discuss interaction between working memory, executive control and attention as they facilitate relational reasoning and abstraction, which we consider to be key cognitive processes in modelling. Eventually, a thorough understanding of modelling cognition can help us to provide better cognitive support for modellers.

From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling

The benefits of enterprise modeling (EM) and its contribution to organizational tasks are largely undisputed in business and information systems engineering. EM as a discipline has been around for several decades but is typically performed by a limited number of people in organizations with an affinity to modeling. What is captured in models is only a fragment of what ought to be captured. Thus, this research note argues that EM is far from its maximum potential. Many people develop some kind of model in their local practice without thinking about it consciously. Exploiting the potential of this “grass roots modeling” could lead to groundbreaking innovations. The aim is to investigate integration of the established practices of modeling with local practices of creating and using model-like artifacts of relevance for the overall organization. The paper develops a vision for extending the reach of EM, identifies research areas contributing to the vision and proposes elements of a future research agenda.

Design patterns for mixed-method research in HCI

In this paper we discuss mixed-method research in HCI. We report on an empirical literature study of the NordiCHI 2012 proceedings which aimed to uncover and describe common mixed-method approaches, and to identify good practices for mixed-methods research in HCI. We present our results as mixed-method research design patterns, which can be used to design, discuss and evaluate mixed-method research. Three dominant patterns are identified and fully described and three additional pattern candidates are proposed. With our pattern descriptions we aim to lay a foundation for a more thoughtful application of, and a stronger discourse about, mixed-method approaches in HCI.

Enterprise Modelling for the Masses – From Elitist Discipline to Common Practice

Enterprise modelling (EM) as a discipline has been around for several decades with a huge body of knowledge on EM in academic literature. The benefits of modelling and its contributions to organizational tasks are largely undisputed. Thus, from an inside-out perspective, EM appears to be a mature and established discipline. However, for initiating serious innovations this view is not sufficient. This position paper takes an outside-in perspective on enterprise modelling and argues that EM is far away from reaching its maximum potential. EM is typically done by a limited number of people in organizations inclined to methods and modelling. What is captured in models is only a fragment of what ought to be captured. Many people actually develop some kind of model in their local practice without thinking about it consciously. Exploiting the potential of this “grass roots modelling” could lead to groundbreaking innovations in EM. The aim is to investigate integration of the established, often systematic and formalized practices of modelling in enterprises with local practices of creating, using and communicating model-like artifacts or objects of relevance for the overall organization.

Flipped Classroom Patterns: Controlling the Pace

During the execution of a flipped classroom course, chances increase students are not at the level scheduled in the initial planning with some students falling behind with their knowledge acquisition and others running ahead. In those cases, adding value during the in-class meetings is much harder for teachers, as they have to take care of all the different knowledge levels. A good way of addressing this problem is to control the learning pace of the group. As a result, the knowledge level of the group will be more homogeneous which makes it easier to add value during the in-class meetings. In previous work we described the pattern Controlling the Pace. This pattern mainly addresses the course planning, but there are more aspects that need to be taken into account. In this paper we propose five patterns based on our experience that can help teachers to control the pace of students learning in various aspects.

On the Identification of Modeler Communities

The authors discuss the use and challenges of identifying communities with shared semantics in Enterprise Modeling (EM). People tend to understand modeling meta-concepts (i.e., a modeling languages constructs or types) in a certain way and can be grouped by this conceptual understanding. Having an insight into the typical communities and their composition (e.g., what kind of people constitute such a semantic community) can make it easier to predict how a conceptual modeler with a certain background will generally understand the meta-concepts s/he uses, which is useful for e.g., validating model semantics and improving the efficiency of the modeling process itself. The authors have observed that in practice decisions to group people based on certain shared properties are often made, but are rarely backed up by empirical data demonstrating their supposed efficacy. The authors demonstrate the use of psychometric data from two studies involving experienced (enterprise) modeling practitioners and computing science students to find such communities. The authors also discuss the challenge that arises in finding common real-world factors shared between their members to identify them by and conclude that there is no empirical support for commonly used (and often implicit) grouping properties such as similar background, focus and modeling language.

An Observation Method for Behavioral Analysis of Collaborative Modeling Skills

Process modeling skills are strongly subject to individual differences in cognitive abilities. However, we lack systematic methods to analyze how psychological mechanisms facilitating cognition influence modeling skills.

Improving student group work with collaboration patterns: a case study

Group work skills are essential for Computer Scientists and especially Software Engineers. Group work is included in most CS curricula in order to support students in acquiring these skills. During group work, problems can occur related to a variety of factors, such as unstable group constellations or (missing) instructor support. Students need to find strategies for solving or preventing such problems. Student collaboration patterns offer a way of supporting students by providing problem-solving strategies that other students have already applied successfully. In this work we describe how student collaboration patterns were applied in an interdisciplinary software engineering project, and show that their application was generally experienced as helpful by the students.