An information assistant system for the prevention of tunnel vision in crisis management

In the crisis management environment, tunnel vision is a set of bias in decision makers’ cognitive process which often leads to incorrect understanding of the real crisis situation, biased perception of information, and improper decisions. The tunnel vision phenomenon is a consequence of both the challenges in the task and the natural limitation in a human being’s cognitive process. An information assistant system is proposed with the purpose of preventing tunnel vision. The system serves as a platform for monitoring the on-going crisis event. All information goes through the system before arrives at the user. The system enhances the data quality, reduces the data quantity and presents the crisis information in a manner that prevents or repairs the user’s cognitive overload. While working with such a system, the users (crisis managers) are expected to be more likely to stay aware of the actual situation, stay open minded to possibilities, and make proper decisions

The effect of multiple knowledge sources on learning and teaching

Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel

The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges

Adaptive development and maintenance of user-centric software systems

A software system cannot be developed without considering the various facets of its environment. Stakeholders – including the users that play a central role – have their needs, expectations, and perceptions of a system. Organisational and technical aspects of the environment are constantly changing. The ability to adapt a software system and its requirements to its environment throughout its full lifecycle is of paramount importance in a constantly changing environment. The continuous involvement of users is as important as the constant evaluation of the system and the observation of evolving environments. We present a methodology for adaptive software systems development and maintenance. We draw upon a diverse range of accepted methods including participatory design, software architecture, and evolutionary design. Our focus is on user-centred software systems

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

Using visual representations to improve instructional materials for distance education computing students

Understanding how to develop instructional materials for distance education students is a challenging problem, but it is exacerbated when a domain is complex to teach, such as computer science. Visual representations have a history of use in computing as a means to alleviate the difficulties of learning abstract concepts. However, it is not clear whether improvements observed are as a result of improvements in the visual representations used in instructional materials or due to individual differences in students. This research examines the two themes of individual differences and visual representation in order to investigate how they collectively impact on improving instructional materials for distance education students studying computer science. It investigates the impact of different representations on learning while additionally investigating the relationship between individual differences and student learning.The research in this thesis shows that visual representations are important in designing instructional materials. In particular, texts with visual representations have the power to cue students to perceive instructional materials as easier to process and more engaging.Investigation into the impact of concrete high-imagery versus abstract low-imagery visual representations illustrated that concrete visual representations incurred fewer cognitive overheads for computer science students and were able to ameliorate the challenges of learning computing.The research in this thesis into individual differences demonstrated that Imagers did benefit more from studying instructional materials containing text with visual components. However the research indicates that appropriate selection of individual difference tests is dependent upon the application, i.e., whether the results are to be used to assess generalised tendencies or episodes in learning and whether the tests examine underlying approaches to cognition or practices in education.An underlying question was whether students studying instructional materials containing low-imagery visual representations would cope as well as those studying high-imagery ones. Accomplished learners demonstrated that they could perform as well as with those receiving high-imagery visual representations. However, studying and recalling these materials did incur more cognitive processing.This thesis argues that improving instructional materials by including appropriate visual representations is a useful basis for improving learning for distance education computer science students

How do Individuals Interpret Multiple Conceptual Models? A Theory of Combined Ontological Completeness and Overlap

When analyzing or designing information systems, users often work with multiple conceptual models because each model articulates a different, partial aspect of a real-world domain. However, the available research in this area has largely studied the use of single modeling artifacts only. We develop a new theory about interpreting multiple conceptual models that details propositions for evaluating how individuals select, understand, and perceive the usefulness of multiple conceptual models. We detail implications of our theory development for empirical research on conceptual modeling. We also outline practical contributions for the design of conceptual models and for choosing models for systems analysis and design tasks. Finally, to stimulate research that builds on our theory, we illustrate procedures for enacting our theory and discuss a range of empirically relevant boundary condition

Construct redundancy in process modelling grammars: Improving the explanatory power of ontological analysis

Conceptual modelling supports developers and users of information systems in areas of documentation, analysis or system redesign. The ongoing interest in the modelling of business processes has led to a variety of different grammars, raising the question of the quality of these grammars for modelling. An established way of evaluating the quality of a modelling grammar is by means of an ontological analysis, which can determine the extent to which grammars contain construct deficit, overload, excess or redundancy. While several studies have shown the relevance of most of these criteria, predictions about construct redundancy have yielded inconsistent results in the past, with some studies suggesting that redundancy may even be beneficial for modelling in practice. In this paper we seek to contribute to clarifying the concept of construct redundancy by introducing a revision to the ontological analysis method. Based on the concept of inheritance we propose an approach that distinguishes between specialized and distinct construct redundancy. We demonstrate the potential explanatory power of the revised method by reviewing and clarifying previous results found in the literature