Abstract: Embodied embedded cognition (EEC) has gained support in cognitive science as well as in human–computer interaction (HCI). EEC can be characterized both by its action-centeredness as well as its roots in phenomenology. The phenomenological aspects of EEC could be seen as support for trends in design emphasizing the user experience. Meanwhile, usability issues often are still approached using traditional methods based on cognitivist assumptions. In this paper, I argue for a renewed focus on improving usability from an EEC perspective. I draw mainly on a behavior-oriented interpretation of the theory, the key aspects of which are reviewed. A tentative sketch for an embodied embedded usability is proposed, doing justice to the embodied embedded nature of interaction while retaining the goal of developing technology that is easy to use in everyday practice.
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From the article: Abstract Over the last decades, philosophers and cognitive scientists have argued that the brain constitutes only one of several contributing factors to cognition, the other factors being the body and the world. This position we refer to as Embodied Embedded Cognition (EEC). The main purpose of this paper is to consider what EEC implies for the task interpretation of the control system. We argue that the traditional view of the control system as involved in planning and decision making based on beliefs about the world runs into the problem of computational intractability. EEC views the control system as relying heavily on the naturally evolved fit between organism and environment. A ‘lazy’ control structure could be ‘ignorantly successful’ in a ‘user friendly’ world, by facilitating the transitory creation of a flexible and integrated set of behavioral layers that are constitutive of ongoing behavior. We close by discussing the types of questions this could imply for empirical research in cognitive neuroscience and robotics.
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This investigation explores relations between 1) a theory of human cognition, called Embodied Cognition, 2) the design of interactive systems and 3) the practice of ‘creative group meetings’ (of which the so-called ‘brainstorm’ is perhaps the best-known example). The investigation is one of Research-through-Design (Overbeeke et al., 2006). This means that, together with students and external stakeholders, I designed two interactive prototypes. Both systems contain a ‘mix’ of both physical and digital forms. Both are designed to be tools in creative meeting sessions, or brainstorms. The tools are meant to form a natural, element in the physical meeting space. The function of these devices is to support the formation of shared insight: that is, the tools should support the process by which participants together, during the activity, get a better grip on the design challenge that they are faced with. Over a series of iterations I reflected on the design process and outcome, and investigated how users interacted with the prototypes.
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Dit essay geeft een systeemvisie op het ontwikkelen van embedded software voor slimme systemen: (mobiele) robots en sensornetwerken.
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The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.
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Computational thinking (CT) has become a necessity in many professional domains. As such, scholars argue that the acquisition of CT and application should be embedded in existing school subjects. Within the CT literature, a tax-onomy distinguishes CT practices in STEM education into four categories: data related, systems thinking, modeling & simulation and computational problem solving (CPSP). Practical applications of these different categories are still limited. This paper presents three examples in which edu-cators of science teachers integrate CT within STEM con-tent knowledge using the above mentioned taxonomy. The first example applies to CPSP and data practices, the sec-ond to CPSP exclusively, the final to systems thinking and modeling & simulation. The examples provide practical insight that makes the use of CT in STEM education more tangible for practitioners.
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The aim of this meta-analysis was to analyze the experimental research into the effects of job-embedded professional development (JEPD) for teachers and student outcomes. Our meta-analysis of experimental studies of the effects JEPD, included 20 studies (with 79 experimental comparisons) at teacher level and 19 studies at student level (with 34 experimental comparisons). Analyses of the studies, representing 2,062 teachers and 21,425 students, revealed a significant, medium-to-large effect size at teacher level (ES= 0.699, SE= 0.092) and a significant medium effect at student level (ES = 0.523, SE= 0.137). Effects for teachers were smaller in studies with a large sample size. Effects for students were positively related to the length of the intervention. The positive outcomes at teacher and student level support the implementation and expansion of JEPD programsacross schools.
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Business model innovations emerge over time and are influenced by managerial interaction with stakeholders. Especially with regard to business model innovation for sustainability, manager-stakeholder interaction can radically change a company’s business model and underlying logic. However, the majority of the literature shows how manager–stakeholder interaction may limit business model innovation when stakeholders reinforce existing managerial cognitions. In this chapter we study how stakeholders can also stimulate business model innovation by affecting managerial cognitive change. Through three case studies, we find that this can occur through three shaping processes: market approach shaping, product/service offering shaping, and credibility shaping. We also find that the impact of new or latent stakeholders is greater than that of existing stakeholders. We end the chapter by sketching a research agenda to further unravel the role of stakeholders affecting managerial cognition around business model innovation for sustainability.
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