Derivation of robust predictor variables for modelling urban shrinkage and its effects at different scales

Currently, we observe diverging processes of growth and shrinkage in European Cities. Whereas in the 80ies and 90ies partially accelerated through the crash of the socialist system mostly urban growth and suburban development occurred in European Cities, today we find a general decline of population as well as an increase of aged people (as results of the demographic change in Europe and worldwide, Cloet 2003, Lutz 2001). These processes influence land use pattern (state of the environment) and land use changes in urban areas enormously. Land use pattern reflect the current socio-economic development of an urban area and give an idea of how the urban ecosystem is influenced by man. In doing so, for instance, surface sealing reduces the filtering and remediation capacity of soils and the water retention in general as well as minimises habitat quality for wetland species. At the same time, the ecosystem(s) provide so-called ecosystem services, benefits people obtain from ecosystems: water availability, drinking water, remediation and filtering of waste, places to settle, recreation facilities in nature and others. Their quantification enables to bring the change (availability/loss) of ecosystem services into relation with effective costs (economic sphere, Farber 2002, De Groot et al. 2002). The above mentioned population decline and related shrinkage processes will have enormous consequences on the demand and availability of ecosystem services needed to sustain a high and even increasing status of quality of life for European citizens in the next future. Therefore, the predictor variables describing on the one hand shrinkage-related land use changes and on the other its effects are most important but at the same time it is still a challenge; to extract such predictor variables from a huge catalogue of urban socio-economic and environmental indicators elaborated by many studies for different landscape types and scales; to derive relevant digital and spatially explicit data as model input to calculate the effects of land use (change) and; to validate the model results at the city and the quarter level (scale) as well as to prove the response of the (gained/released) ecosystem service (environmental quality) at the city and at quarter level (closing the circle). Here, the author will give some expressive examples showing the derivation of predictor variables for modelling peri-urban growth and inner city shrinkage as well as its effects on water balance, habitat quality (urban green network) and recreational space. Of major interest is the approach of how to tackle the problem of urban shrinkage in spatially explicit land use (change) modelling (Haase et al. 2004).

Models for Learning (Mod4L) Final Report: Representing Learning Designs

The Mod4L Models of Practice project is part of the JISC-funded Design for Learning Programme. It ran from 1 May – 31 December 2006. The philosophy underlying the project was that a general split is evident in the e-learning community between development of e-learning tools, services and standards, and research into how teachers can use these most effectively, and is impeding uptake of new tools and methods by teachers. To help overcome this barrier and bridge the gap, a need is felt for practitioner-focused resources which describe a range of learning designs and offer guidance on how these may be chosen and applied, how they can support effective practice in design for learning, and how they can support the development of effective tools, standards and systems with a learning design capability (see, for example, Griffiths and Blat 2005, JISC 2006). Practice models, it was suggested, were such a resource. The aim of the project was to: develop a range of practice models that could be used by practitioners in real life contexts and have a high impact on improving teaching and learning practice. We worked with two definitions of practice models. Practice models are: 1. generic approaches to the structuring and orchestration of learning activities. They express elements of pedagogic principle and allow practitioners to make informed choices (JISC 2006) However, however effective a learning design may be, it can only be shared with others through a representation. The issue of representation of learning designs is, then, central to the concept of sharing and reuse at the heart of JISC’s Design for Learning programme. Thus practice models should be both representations of effective practice, and effective representations of practice. Hence we arrived at the project working definition of practice models as: 2. Common, but decontextualised, learning designs that are represented in a way that is usable by practitioners (teachers, managers, etc).(Mod4L working definition, Falconer & Littlejohn 2006). A learning design is defined as the outcome of the process of designing, planning and orchestrating learning activities as part of a learning session or programme (JISC 2006). Practice models have many potential uses: they describe a range of learning designs that are found to be effective, and offer guidance on their use; they support sharing, reuse and adaptation of learning designs by teachers, and also the development of tools, standards and systems for planning, editing and running the designs. The project took a practitioner-centred approach, working in close collaboration with a focus group of 12 teachers recruited across a range of disciplines and from both FE and HE. Focus group members are listed in Appendix 1. Information was gathered from the focus group through two face to face workshops, and through their contributions to discussions on the project wiki. This was supplemented by an activity at a JISC pedagogy experts meeting in October 2006, and a part workshop at ALT-C in September 2006. The project interim report of August 2006 contained the outcomes of the first workshop (Falconer and Littlejohn, 2006). The current report refines the discussion of issues of representing learning designs for sharing and reuse evidenced in the interim report and highlights problems with the concept of practice models (section 2), characterises the requirements teachers have of effective representations (section 3), evaluates a number of types of representation against these requirements (section 4), explores the more technically focused role of sequencing representations and controlled vocabularies (sections 5 & 6), documents some generic learning designs (section 8.2) and suggests ways forward for bridging the gap between teachers and developers (section 2.6). All quotations are taken from the Mod4L wiki unless otherwise stated

Eliciting Expertise

Since the last edition of this book there have been rapid developments in the use and exploitation of formally elicited knowledge. Previously, (Shadbolt and Burton, 1995) the emphasis was on eliciting knowledge for the purpose of building expert or knowledge-based systems. These systems are computer programs intended to solve real-world problems, achieving the same level of accuracy as human experts. Knowledge engineering is the discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems (Schreiber et al., 2000) This chapter will discuss the problem of knowledge elicitation for knowledge intensive systems in general

Recent Conceptual Consequences of Loop Quantum Gravity. Part I: Foundational Aspects

Conceptual consequences of recent results in loop quantum gravity are collected and discussed here in view of their implications for a modern philosophy of science which is mainly understood as one that totalizes scientific insight so as to eventually achieve a consistent model of what may be called fundamental heuristics on an onto-epistemic background which is part of recently proposed transcendental materialism. This enterprise is being understood as a serious attempt of answering recent appeals to philosophy so as to provide a conceptual foundation for what is going on in modern physics, and of bridging the obvious gap between physics and philosophy. This present first part of the paper deals with foundational aspects of this enterprise, a second part will deal with its holistic aspects.Comment: 25 page

Mind-reading versus neuromarketing: how does a product make an impact on the consumer?

Purpose – This research study aims to illustrate the mapping of each consumer’s mental processes in a market-relevant context. This paper shows how such maps deliver operational insights that cannot be gained by physical methods such as brain imaging. Design/methodology/approach – A marketed conceptual attribute and a sensed material characteristic of a popular product were varied across presentations in a common use. The relative acceptability of each proposition was rated together with analytical descriptors. The mental interaction that determined each consumer’s preferences was calculated from the individual’s performance at discriminating each viewed sample from a personal norm. These personal cognitive characteristics were aggregated into maps of demand in the market for subpanels who bought these for the senses or for the attribute. Findings – Each of 18 hypothesized mental processes dominated acceptance in at least a few individuals among both sensory and conceptual purchasers. Consumers using their own descriptive vocabulary processed the factors in appeal of the product more centrally. The sensory and conceptual factors tested were most often processed separately, but a minority of consumers treated them as identical. The personal ideal points used in the integration of information showed that consumers wished for extremes of the marketed concept that are technologically challenging or even impossible. None of this evidence could be obtained from brain imaging, casting in question its usefulness in marketing. Research limitations/implications – Panel mapping of multiple discriminations from a personal norm fills three major gaps in consumer marketing research. First, preference scores are related to major influences on choices and their cognitive interactions in the mind. Second, the calculations are completed on the individual’s data and the cognitive parameters of each consumer’s behavior are aggregated – never the raw scores. Third, discrimination scaling puts marketed symbolic attributes and sensed material characteristics on the same footing, hence measuring their causal interactions for the first time. Practical implications – Neuromarketing is an unworkable proposition because brain imaging does not distinguish qualitative differences in behavior. Preference tests are operationally effective when designed and analyzed to relate behavioral scores to major influences from market concepts and sensory qualities in interaction. The particular interactions measured in the reported study relate to the major market for healthy eating. Originality/value – This is the first study to measure mental interactions among determinants of preference, as well as including both a marketed concept and a sensed characteristic. Such an approach could be of great value to consumer marketing, both defensively and creatively

Learning to teach ideas and evidence in science: a study of school mentors and trainee teachers

This article reports on a small-scale evaluation of how beginning teachers undertaking a PGCE in secondary science worked collaboratively with their school based mentors to enhance practice in the use of ideas and evidence in science. Mentors and beginning teachers were introduced to the resources and teaching strategies previously developed at King’s College London as part of the Nuffield funded IDEAS curriculum development project (Osborne, Erduran & Simon, 2004a). The judicious selection of resources and strategies from the IDEAS pack formed the basis of mentors’ workshops, where mentors were encouraged to put into practice IDEAS and other argumentation activities and strategies. Collaborative work with their mentors enabled the BTs to initiate their teaching of ideas and evidence. They experienced both positive aspects and limitations when attempting IDEAS activities in their science classrooms

What Can Be Learned from Computer Modeling? Comparing Expository and Modeling Approaches to Teaching Dynamic Systems Behavior

Computer modeling has been widely promoted as a means to attain higher order learning outcomes. Substantiating these benefits, however, has been problematic due to a lack of proper assessment tools. In this study, we compared computer modeling with expository instruction, using a tailored assessment designed to reveal the benefits of either mode of instruction. The assessment addresses proficiency in declarative knowledge, application, construction, and evaluation. The subscales differentiate between simple and complex structure. The learning task concerns the dynamics of global warming. We found that, for complex tasks, the modeling group outperformed the expository group on declarative knowledge and on evaluating complex models and data. No differences were found with regard to the application of knowledge or the creation of models. These results confirmed that modeling and direct instruction lead to qualitatively different learning outcomes, and that these two modes of instruction cannot be compared on a single “effectiveness measure”