A methodology for the capture and analysis of hybrid data: a case study of program debugging

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Knowledge Transfer Between Languages and Paradigms

As the field of Information Systems (IS) continues to advance, organizations will constantly be facing the challenge of paradigm shifts. This paper extends prior work on IS personnel training by emphasizing the retraining of IS professionals during a paradigm shift. The application of the Osgood (1949) transfer surface to the software development domain is proposed. A model is developed that defines the concepts of positive, negative and no skill transfer within the software development domain. This model demonstrates that the similarity of the stimuli and responses of the software development projects can predict the direction of the skill transfer. Anecdotal evidence of critical points in the model is provided to support applicability to the software development domain. This paper provides a theoretical beginning for work on paradigm shifts in the software development domain and highlights the importance of knowledge transfer in the face of technological change

Associations and Mutual Properties - An Experimental Assessment

Associations are a widely used construct of object-oriented languages. However, the meaning of associations for conceptual modelling of application domains remains unclear. Ontological considerations in past research suggest that associations are related to the concept of mutual properties. Specifically, previous research has suggested that mutual properties, not associations, should be modelled, and guidelines for doing this in UML have been offered. This paper presents the results of an experimental study, which suggest that this guidance does in fact lead to improved models

An examination of the complexity and comprehensibility of various software models

In a discussion of the creation and evolution of the statechart, David Harel, the creator of the model, talks about his primary goals for making a good model. He emphasizes that a good model should be clear, precise, visualizable, and executable. A clear model is one that can be easily understood by someone unfamiliar with the system being modeled. A precise model shows the entirety of the system under consideration, without including extraneous information. A visualizable model is one that can be interpreted by a user using primarily visual information, meaning some words must exist for thing like labels, but the majority of the information contained in the model is expressed through symbols that are easily understandable. An executable model is one that can be developed and then applied to help users understand or even create the system that is modeled. These ideas are indeed very important to the usefulness of a model, as a model that is lacking in one or more of these areas could be difficult to understand, use, or both. Today, however, there are dozens of different models that aim to show different aspects of systems, and many of these models may not have been designed with the same goals that Harel outlines. This paper aims to examine the complexity of various software models, including UML models and other commonly used models. I will look at each model and attempt to determine areas of the model that are particularly hard to understand, or areas where ambiguity is possible within the model. I will discuss the implications that model complexity has on a persons ability to learn the model and to recall it at a later date. I will also discuss ways to improve a persons ability to remember the aspects of a model when they are an infrequent user or when they have not encountered that model in a long time

Pair modeling with DynaLearn - Students' attitudes and actual effects

With DynaLearn learners can construct scientific knowledge by manipulating icons and their in-ter-relationships, using a diagrammatic representation. The diagrams represent models that can be simulated, confronting learners with the logical consequences of the knowledge they expressed. Such modeling activities are highly advocated by science educators. Learning from the construction and debugging processes of modeling can be enhanced by collaboration. The modeling elements can serve as anchors for discussing, justifying, and explaining the model. Researchers have suggested various ways of supporting collaboration. In this study we employed Pair Modeling, which is an adaptation of the pair programming technique that is used for enhancing collaborative programming both in the industry and in academia. In this paper we present encouraging results for the use of this collaboration technique based on assignments' scores, observations, and a ques-tionnaire. Students' attitudes were neutral on the average, but the average score of the group that employed Pair Modeling was significantly higher than the average score of the control group that employed unstructured pair collaboration. We discuss the implications of the obtained results and the limitations of the study