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Series:

Jan H.G. Klabbers

The purpose of this unique book is to outline the core of game science by presenting principles underlying the design and use of games and simulations. Game science covers three levels of discourse: the philosophy of science level, the science level, and the application or practical level. The framework presented will help to grasp the interplay between forms of knowledge and knowledge content, interplay that evolves through the action of the players.
Few scientists have witnessed such a radical change in their area of research and practice as those who engaged in play and gaming since the 1950s. Since that time game scientists from a whole variety of disciplines started adopting gaming and simulation methods in their research. Rapid advances in information technology and computer science are producing a tool rich environment for the design and use of games, and for humanities studies of games as digital arts and interactive narratives. Game science is advancing through these waves of change, driven by the digital computer game industry, enhanced through computer and information science, as well as through advances in professional gaming such as in education, public and business management, policy development, health care, eco-systems management, and so on.
When asking game scientists about the core of their science, one should expect to hear diverging answers. The common questions about the core of game and play are not new. They refer to: What is the meaning of game and play? What is real and what is virtual reality? How could we build simple and effective games from complex social systems? Are we able to bring forward a general theory of games? Are we able to help players (social actors) to find smart solutions and approaches to complex issues? How do games enhance learning and how do they improve our thinking capacity and action repertoire?
Current answers to these questions are scattered and inadequate. This book offers a frame-of-reference that will enlighten the characteristics of particular games and simulations from a common perspective. The author pays less attention to instrumental reasoning than on theoretical and methodological questions. Answers will provide a suitable context for addressing design science and analytical science approaches to artifact design and assessment, and theory development and testing. Due to the high diversity of approaches that game science has to accommodate the author chooses an interdisciplinary and where appropriate a meta-disciplinary approach.

Series:

Douglas M. Towne

This volume presents an object-oriented approach for developing interactive graphical device models and for delivering instruction and performance aiding with such models. The volume attempts to illustrate, via a series of examples, why and how the particular design given satisfies relatively intensive and diverse instructional and performance-aiding demands with surprising ease.
The early chapters focus on the fundamental design concepts upon which all applications stand, including a consistent design of the basic elements - objects - from which all models are produced; a clear separation between the model of the target domain and the instructional processes; and, wherever possible, automatic generation of user interactions, based on the structure and content of the model.
Each of the later chapters focus on one particular application area, including explication of complex system functions, diagnostic instruction and guidance, procedural guidance, scenario-based instruction, and simulation-based technical documentation.
The volume is intended to serve instructional designers, curriculum developers, and software implementers, an ambitious scope that is hopefully achieved via the early presentation of critical “nuts-and-bolts”, followed by discussions of specific training and aiding environments that can be more selectively considered. The more complex examples presented in the volume are available for active operation and analysis in a Web site developed for the reader’s use.

Series:

Sylvia P. van Borkulo, Wouter R. van Joolingen, Elwin R. Savelsbergh and Ton de Jong

Learning by computer modeling is claimed to yield learning gains in the fields of knowledge of dynamic systems, higher-order reasoning, and domain-specific knowledge. However, it is hard to substantiate these claims with objective test measures. It is our aim to develop a test for these knowledge types that is able to detect differential learning outcomes between different modes of instruction (e.g. modeling, learning with simulation, and expository teaching). We present a framework to distinguish learning outcomes on three dimensions: type of reasoning process, complexity of knowledge elements, and domain-specificity. Based on this framework, we propose a test with specific items for each of the resulting combinations.

Series:

Stefanie Hillen and Jose Gonzalez

We assume that the application of ‘dynamic stories’ supports the understanding of complex simulation models. Triggered by a dynamic story non-computer-experts are enabled to grasp crucial content captured in system dynamics models (sd). One target of our research project is to disseminate insights of Information Security issues to organizations in the gas and oil-industry. To write reliable and meaningful dynamic stories one needs modeling and instructional design expertise. We opt for the term dynamic stories to stress that, on the one hand, they are generated from system dynamics models and, on the other hand, they represent issues over time. The ‘Dynamic Story Triangle’ is used as a framework to develop such stories. The exemplarily presented dynamic story is telling about insights derived from the sdmodel ‘effective use of a new technology’. The sd-model we refer to is based on the data we received from our client. Regardless the confidentiality we have to respect the dynamic story is situated in a specific and authentic context.

Re-Presenting Canadian History On-line

“The Cyberterrorism Crisis” Web Site as a Test Case of History and Citizenship Education on the Web

Series:

Kevin Kee

Series:

Andreas Harrer, Lars Bollen and Ulrich Hoppe

This article discusses different facets of collaborative model construction, specifically the graphical modeling with visual languages. We explain the concept of collaborative mindtools, a synergy between mindtools as “objects to think with” and collaborative learning. We define a classification schema to explore the possible space of collaborative learning according to the dimensions of varying social arrangements and different interaction modes. Our practical experience in some of the resulting scenarios is described to highlight the possible varieties of collaborative modeling scenarios. We conclude with an integrated scenario utilizing several of these arrangements and present approaches and tools to support teachers in orchestrating these scenarios.

Trails as Part of Curriculum

On the Potential Synergy of Planning and Navigation in Learning

Series:

Judith Schoonenboom

In this chapter we argue that the study of trails can benefit from incorporating concepts and insights from curriculum studies and vice versa. Trails are often presented in the context of navigation by the learner, but we observe that this is not the only way in which trails can be used. The wider perspective of curriculum studies is introduced, with its recognition of the several levels, activities and actors that are involved in working on curriculum. We show that incorporating this perspective widens the scope from the learner navigating through learning objects to include actors such as curriculum developers, teachers and researchers, in their different activities on planning, going through and analyzing curriculum activities.

Yet, the study of trails is also important to curriculum studies. We show that curriculum is undergoing several changes, into which the study of trails fits well. Basically, the curriculum becomes more open, and less fixed for the learner. This puts an emphasis on navigation by the learner. Also, more attention is paid to reflection, and this includes learners reflecting on their own trails.

On the basis of this framework, we present a classification of trails, which incorporates elements originating from trails studies and curriculum studies. Finally, we discuss the metadata that are needed in order to work with trails.

Series:

Tristan E. Johnson and Eric G. Sikorski

This chapter describes the design and development of a training algorithm that is be used to model the instructional effects on task performance. Empirical studies provide evidence as to which instructional strategies are most effective for given maintenance tasks and to identify the key factors (e.g., prior-experience, level of difficulty of content, skill level, motivation, etc.) that are strongly correlated in modeling maintenance training. Data from these studies are integrated into an existing manpower training and development database in the form of training algorithms that are appropriate for specific training tasks and that estimate required training time.

The project purpose was to create a model of the relationships between various learner factors coupled with specific instructional strategies. Based on the literature and data analysis, several factors were selected that have an impact on task performance. We present the phases involved in model development. These phases include: Phase 1: Select Instructional Strategies Framework; Phase 2: Select Representative Learning and Performance Tasks; Phase 3: Select Key Input Factors; Phase 4: Conduct Empirical Study; and Phase 5: Develop Training Algorithm and Model.

Visualising Trails

Supporting Curriculum Activities by Making Trails Visible

Series:

Judith Schoonenboom

This chapter shows how visualisation can support working with trails. Six very diverse cases of the visualisation of trails are described. The selection of the cases is based on the inclusive view on trails, presented in Chapter 1. In this view, trails may support diverse curriculum actors (learners, teachers, curriculum developers), consist of various trail elements ( e.g. learning materials, learning results or learning objectives), and the rationale behind ordering the elements of the trails may be a chronological path, but also the relevance of learning materials to the learner. Also, both planning trails, following trails and reflecting on trails are considered. This chapter shows that visualisation of trails can be useful in these various circumstances.

Personalised Trails

How Machines Can Learn to Adapt Their Behaviour to Suit Individual Learners

Series:

Kevin Keenoy, Mark Levene and Sara de Freitas

This chapter discusses how the trails concept can be applied in providing personalised learning, with a focus on the use of trails in adaptive e-leaming systems which adjust their behaviour in order to better cater for individual learners' needs.

The first half of the chapter looks at some of the technological and theoretical issues posed by personalised trails: We present a generic architecture for personalisation which can potentially be deployed on a variety of hardware configurations, and then look at learner profiling, a key aspect of personalisation. Leamer profiles store the information about learners that is used to provide personalisation, so the structure and content of the learner profile used by a system has implications for the kinds of personalisation and support for trails that can be provided. In this context we see how Semantic Web technologies can potentially be used to integrate disparate user profile specifications.

In the second half of the chapter we look at the practicalities and practice of personalising trails in the context of e-leaming. After a brief general discussion of some techniques that can be used to provide personalisation we use examples of real systems to demonstrate the potential for providing personalised trail support that currently exists. As well as systems that provide trails for learners our examples include adaptive hypertext techniques which provide a kind of trail support through adaptive presentation; systems that provide adaptive feedback to learners and which put learners in direct control of the adaptivity; social navigation systems and mobile computing systems. We conclude with some thoughts about the future of this burgeoning research area.