Crime script analysis as a methodology to analyse criminal processes is underdeveloped. This is apparent from the various approaches in which scholars apply crime scripting and present their cybercrime scripts. The plethora of scripting methods raise significant concerns about the reliability and validity of these scripting studies. In this methodological paper, we demonstrate how object-oriented modelling (OOM) could address some of the currently identified methodological issues, thereby refining crime script analysis. More specifically, we suggest to visualise crime scripts using static and dynamic modelling with the Unified Modelling Language (UML) to harmonise cybercrime scripts without compromising their depth. Static models visualise objects in a system or process, their attributes and their relationships. Dynamic models visualise actions and interactions during a process. Creating these models in addition to the typical textual narrative could aid analysts to more systematically consider, organise and relate key aspects of crime scripts. In turn, this approach might, amongst others, facilitate alternative ways of identifying intervention measures, theorising about offender decision-making, and an improved shared understanding of the crime phenomenon analysed. We illustrate the application of these models with a phishing script.
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In summarizing the research on collaborative learning, the quest for the holy grail of effective collaborative learning has not yet ended. The use of the GLAID framework tool for the design of collaborative learning in higher education may contribute to better aligned designs and hereby contribute to more effective collaborative learning. The GLAID framework may help monitor, evaluate and redesign projects and group assignments. We know that the perception of the quality of the task, and the extent to which students feel engaged, influences the perception of students of how much they learn from a GLA. However, perceptions alone are only an indication of what is learned. A next step is to study exactly what those learning outcomes are. This leads to a more difficult question: how can we measure the learning outcomes? Although a variety of research underlines the large potential of collaboration for learning outcomes, the exact learning outcomes of team learning can only be partly foretold. During collaborative learning students could partly achieve the same or similar learning outcomes, but as each individual learning internalizes what is learned from the collaborative learning by his/her given prior experiences and knowledge, the learning outcomes of collaborative learning are probabilistic (Strijbos, 2011), and therefore attaining specific learning outcomes is likely but not guaranteed. If learning outcomes are different per individual and are probabilistic, how can we measure those learning outcomes? Wenger, Trayner, & De Laat (2011) regard the outcomes of learning communities as value creations that have an individual outcome and a group outcome. This value creation induced by collaborative learning consists, for example, of changed behaviour in the working environment as well as the production of useful products or artefacts. Tillema (2006) also describes that communities of inquiry can lead to the design of conceptual artefacts: products that are useful for a professional working environment.
Introduction: Different models of frameworks for dietetic care are used in Europe. There is a substantial need for a consistent framework to compare research results and to cooperate on an international level. Therefore, one of the goals of the EU-funded project IMPECD was the development of a unified framework Dietetic Care Process (DCP) in order to foster a shared understanding of process-driven dietetic counselling. Materials and Methods:: Based on a literature review and in-depth analysis of different frameworks an iterative and incremental development process of finding solutions for decision-making within the consortium consisting of dietetic experts from 5 European HEI was passed. The developed DCP model was integrated in an online training course including 9 clinical cases (MOOC) to train students. The draft versions and the concluding final version DCP model were evaluated and re-evaluated by teachers and 25 students at two Intensive Study Programmes. Results:: The DCP model consists of five distinct, interrelated steps which the consortium agreed on: Dietetic Assessment, Dietetic Diagnosis, Planning Dietetic Intervention, Implementing Dietetic Intervention, Dietetic Outcome Evaluation. A standardized scheme was developed to define the process steps: dedication, central statement, aim and principles, and operationalization. Discussion:: Existing different process models were analyzed to create a new and consistent concept of a unified framework DCP. The variety within the European countries represented by the consortium proved to be both a challenge in decision-making and an opportunity to integrate multinational perspectives and intensify the scientific discourse. The development of a standardized scheme with precise definitions is a prerequisite for planning study designs in health services research. Besides, clarification is essential for establishing process-guided work in practice. The evaluated MOOC is now implemented in study programmes used by 5 European HEI in order to keep approaches and process-driven action comparable. The MOOC promotes the exchange of ideas between future professionals on an international level.
The focus of the research is 'Automated Analysis of Human Performance Data'. The three interconnected main components are (i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis . Human Performance is both the process and result of the person interacting with context to engage in tasks, whereas the performance range is determined by the interaction between the person and the context. Cheap and reliable wearable sensors allow for gathering large amounts of data, which is very useful for understanding, and possibly predicting, the performance of the user. Given the amount of data generated by such sensors, manual analysis becomes infeasible; tools should be devised for performing automated analysis looking for patterns, features, and anomalies. Such tools can help transform wearable sensors into reliable high resolution devices and help experts analyse wearable sensor data in the context of human performance, and use it for diagnosis and intervention purposes. Shyr and Spisic describe Automated Data Analysis as follows: Automated data analysis provides a systematic process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions and supporting decision making for further analysis. Their philosophy is to do the tedious part of the work automatically, and allow experts to focus on performing their research and applying their domain knowledge. However, automated data analysis means that the system has to teach itself to interpret interim results and do iterations. Knuth stated: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it.[Knuth, 1974]. The knowledge on Human Performance and its Monitoring is to be 'taught' to the system. To be able to construct automated analysis systems, an overview of the essential processes and components of these systems is needed.Knuth Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.
The objective of the SIA KIEM proposal Capturing Value is to understand financial decision making and capturing value as a logical step in the conceptual and realization phase of creative products. We will explore the narrative of reasoning and making choices in the use of different financial instruments by creative professionals. These narratives are telling about the values creative professionals attach to financial decision making and how these influence the choice of financial instruments. Most research focusses either on the (non)availability of financial instruments or on the use of these instruments. This research proposal focusses on the missing link: how do creative professionals reason when confronted with making financial decisions? Which options do they consider and how are these influenced by their attitude towards and knowledge of various formal and informal financial instruments? The project is a first step to develop a bigger and international research proposal on the way artists and creatives capture the financial value of their creations.
The energy transition is a highly complex technical and societal challenge, coping with e.g. existing ownership situations, intrusive retrofit measures, slow decision-making processes and uneven value distribution. Large scale retrofitting activities insulating multiple buildings at once is urgently needed to reach the climate targets but the decision-making of retrofitting in buildings with shared ownership is challenging. Each owner is accountable for his own energy bill (and footprint), giving a limited action scope. This has led to a fragmented response to the energy retrofitting challenge with negligible levels of building energy efficiency improvements conducted by multiple actors. Aggregating the energy design process on a building level would allow more systemic decisions to happen and offer the access to alternative types of funding for owners. “Collect Your Retrofits” intends to design a generic and collective retrofit approach in the challenging context of monumental areas. As there are no standardised approaches to conduct historical building energy retrofits, solutions are tailor-made, making the process expensive and unattractive for owners. The project will develop this approach under real conditions of two communities: a self-organised “woongroep” and a “VvE” in the historic centre of Amsterdam. Retrofit designs will be identified based on energy performance, carbon emissions, comfort and costs so that a prioritisation strategy can be drawn. Instead of each owner investing into their own energy retrofitting, the neighbourhood will invest into the most impactful measures and ensure that the generated economic value is retained locally in order to make further sustainable investments and thus accelerating the transition of the area to a CO2-neutral environment.
