Barbara Kump

On the Beauty and Usability of Tag Clouds

Tag clouds are text-based visual representations of a set of tags usually depicting tag importance by font size. Recent trends in social and collaborative software have greatly increased the popularity of this type of visualization. This paper proposes a family of novel algorithms for tag cloud layout and presents evaluation results obtained from an extensive user study and a technical evaluation. The algorithms address issues found in many common approaches, for example large whitespaces, overlapping tags and restriction to specific boundaries. The layouts computed by these algorithms are compact and clear, have small whitespaces and may feature arbitrary convex polygons as boundaries. The results of the user study and the technical evaluation enable designers to devise a combination of algorithm and parameters which produces satisfying tag cloud layouts for many application scenarios.

MoKi: The Enterprise Modelling Wiki

Enterprise modelling focuses on the construction of a structured description, the so-called enterprise model , which represents aspects relevant to the activity of an enterprise. Although it has become clearer recently that enterprise modelling is a collaborative activity, involving a large number of people, most of the enterprise modelling tools still only support very limited degrees of collaboration. Within this contribution we describe a tool for enterprise modelling, called MoKi (MOdelling wiKI), which supports agile collaboration between all different actors involved in the enterprise modelling activities. MoKi is based on a Semantic Wiki and enables actors with different expertise to develop an enterprise model not only using structural (formal) descriptions but also adopting more informal and semi-formal descriptions of knowledge.

Reflection as a Catalyst for Organisational Learning.

This article takes a psychological perspective on organisational learning, putting reflection into the centre of attention. We argue that (1) organisational learning is based on individual and team learning at work, (2) reflection is the driving force that leads to organisational learning and (3) cumulation of the staffs reflection outcomes allows for integration of individual and team learning into organisational best practice. Building on a vast amount of theory in this area, we provide a refined psychological model of the socio-cognitive processes of reflective learning within organisations including the initiation and the termination of reflection. Further, we discuss in depth the role of reflection for organisational learning in the light of recent theories of organisational learning.

Recommending Knowledgeable People in a Work-Integrated Learning System

According to studies into learning at work, interpersonal help seeking is the most important strategy of how people acquire knowledge at their workplaces. Finding knowledgeable persons, however, can often be difficult for several reasons. Expert finding systems can support the process of identifying knowledgeable colleagues thus facilitating communication and collaboration within an organization. In order to provide the expert finding functionality, an underlying user model is needed that represents the characteristics of each individual user. In our article we discuss requirements for user models for the workintegrated learning (WIL) situation. Then, we present the APOSDLE People Recommender Service which is based on an underlying domain model, and on the APOSDLE User Model. We describe the APOSDLE People Recommender Service on the basis of the Intuitive Domain Model of expert finding systems, and explain how this service can support interpersonal help seeking at workplaces.

Seeing what the system thinks you know: visualizing evidence in an open learner model

User knowledge levels in adaptive learning systems can be assessed based on user interactions that are interpreted as Knowledge Indicating Events (KIE). Such an approach makes complex inferences that may be hard to understand for users, and that are not necessarily accurate. We present MyExperiences, an open learner model designed for showing the users the inferences about them, as well as the underlying data. MyExperiences is one of the first open learner models based on tree maps. It constitutes an example of how research into open learner models and information visualization can be combined in an innovative way.

A methodology for eliciting, modelling, and evaluating expert knowledge for an adaptive work-integrated learning system

We present a methodology for constructing and evaluating models for adaptive informal technology-enhanced workplace learning. It is designed for knowledge-intensive work domains which are not pre structured according to a fixed curriculum. We extend research on Competence-based Knowledge Space Theory which has been mainly applied in educational settings. Our approach employs systematic knowledge elicitation and practically feasible evaluation techniques performed as part of the modelling process for iterative refinement of the models. A case study was performed in the Requirements Engineering domain to apply and test the developed methodology. We discuss lessons learned and several implications for knowledge engineering for adaptive workplace learning.

Knowledge Services for Work-Integrated Learning

In order to support work-integrated learning scenarios task- and competency-aware knowledge services are needed. In this paper we introduce three key knowledge services of the APOSDLE system and illustrate how they interact. The context determination daemon observes user interactions and infers the current work task of the user. The user profile service uses the identified work tasks to determine the competences of the user. And finally, the associative retrieval service utilizes both the current work task and the inferred competences to identify relevant (learning) content. All of these knowledge services improve through user feedback.

Which User Interactions Predict Levels of Expertise in Work-Integrated Learning?

Predicting knowledge levels from users implicit interactions with an adaptive system is a difficult task, particularly in learning systems that are used in the context of daily work tasks. We have collected interactions of six persons working with the adaptive work-integrated learning system APOSDLE over a period of two months to find out whether naturally occurring interactions with the system can be used to predict their level of expertise. One set of interactions is based on the tasks they performed, the other on a number of additional Knowledge Indicating Events KIE. We find that the addition of KIE significantly improves the prediction as compared to using tasks only. Both approaches are superior to a model that uses only the frequencies of events.

Providing varying degrees of guidance for work-integrated learning

We present a work-integrated learning (WIL) concept which aims at empowering employees to learn while performing their work tasks. Within three usage scenarios we introduce the APOSDLE environment which embodies the WIL concept and helps knowledge workers move fluidly along the whole spectrum of WIL activities. By doing so, they are experiencing varying degrees of learning guidance: from building awareness, over exposing knowledge structures and contextualizing cooperation, to triggering reflection and systematic competence development. Four key APOSDLE components are responsible for providing this variety of learning guidance. The challenge in their design lies in offering learning guidance without being domain-specific and without relying on manually created learning content. Our three month summative workplace evaluation within three application organizations suggests that learners prefer awarenss building functionalities and descriptive learning guidance and reveals that they benefited from it.

Cognitive foundations of organizational learning: re-introducing the distinction between declarative and non-declarative knowledge.

Contemporary research into socio-cognitive foundations of organizational learning tends to disregard the distinction between declarative and non-declarative knowledge. By reviewing the literature from organizational learning research and cognitive psychology we explain that this distinction is crucial. We describe the foundations of organizational learning by referring to models that consider the interplay between individual and collective knowledge-related processes in organizations. We highlight the existence of a research gap resulting from the finding that these approaches have widely neglected the existence of different types of knowledge. We then elaborate on characteristics of declarative and non-declarative knowledge in general, consider organizations as structures of distributed cognition, and discuss the relationship between organizational knowledge and practice. Subsequently, we examine the role of declarative and non-declarative knowledge in the context of organizational learning. Here, we analyze (1) the cognitive and social mechanisms underlying the development of declarative and non-declarative knowledge within structures of distributed cognition; and (2) the relationship between alterations in declarative and non-declarative types of knowledge on the one hand and changes in organizational practice on the other. Concluding, we discuss implications of our analysis for organizational learning research. We explain how our integrative perspective may offer starting points for a refined understanding of the sub-processes involved in organizational learning and unlearning and may support a better understanding of practical problems related to organizational learning and change.