This paper highlights the use of State Space Grids (SSGs) for studying real-time classroom discourse in an intervention targeting professional development. State Space Grid analysis is both a powerful way to visualise patterns in interactional data, and a starting point for further quantitative analysis. In the present study SSGs were used to explore patterns in teacher–student interactions. The study shows the importance of using micro-level time-serial data and illustrates how change in interactions during and after an intervention can be studied. SSG analysis was applied to study interaction in terms of the coupling of a teacher and a student variable: autonomy support and musical creativity. Video data from 40 music lessons of five teachers and their classes was used as input for plotting teacher–student interactions in SSGs, consisting of two dimensions. SSGs allow visualising change in the situation of interactions in the grid and identifying change in patterns to different grid areas. The findings show how interactions tended to settle in areas representing more productive interaction for all but one class. We discuss the benefits of using SSGs in intervention studies and the implications for educational practice and research of using this time-serial approach.
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Like the professionals, design students tend to avoid the complexity of the user context, and moral issues are largely overlooked. This inspired us to explore whether we could engage design students in thinking about moral issues by exploring different ethical frameworks in their designing. As a case environment we chose smart-grid product service combinations. In this paper we first discuss the ethical frameworks of four selected philosophers’: Plato, Rousseau, Kant, & Mill. Then we will describe the student design process, the resulting four smart grid service concepts and the user insights that came from a user evaluation. We discuss how this approach allowed the students to get insights in their own ethical stance and how they allowed users to reflect on possible futures. We also discuss how these ‘probing’ concepts were used within the larger smart grid project.
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The application of DC grids is gaining more attention in office applications. Especially since powering an office desk would not require a high power connection to the main AC grid but could be made sustainable using solar power and battery storage. This would result in fewer converters and further advanced grid utilization. In this paper, a sustainable desk power application is described that can be used for powering typical office appliances such as computers, lighting, and telephones. The desk will be powered by a solar panel and has a battery for energy storage. The applied DC grid includes droop control for power management and can either operate stand-alone or connected to other DC-desks to create a meshed-grid system. A dynamic DC nano-grid is made using multiple self-developed half-bridge circuit boards controlled by microcontrollers. This grid is monitored and controlled using a lightweight network protocol, allowing for online integration. Droop control is used to create dynamic power management, allowing automated control for power consumption and production. Digital control is used to regulate the power flow, and drive other applications, including batteries and solar panels. The practical demonstrative setup is a small-sized desktop with applications built into it, such as a lamp, wireless charging pad, and laptop charge point for devices up to 45W. User control is added in the form of an interactive remote wireless touch panel and power consumption is monitored and stored in the cloud. The paper includes a description of technical implementation as well as power consumption measurements.
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Studying real-time teacher-student interaction provides insight into student's learning processes. In this study, upper grade elementary teachers were supported to optimize their instructional skills required for co-constructing scientific understanding. First, we examined the effect of the Video Feedback Coaching intervention by focusing on changes in teacher-student interaction patterns. Second, we examined the underlying dynamics of those changes by illustrating an in-depth micro-level analysis of teacher-student interactions. The intervention condition showed significant changes in the way scientific understanding was co-constructed. Results provided insight into how classroom interaction can elicit optimal co-construction and how this process changes during an intervention.
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Author supplied: "This paper gives a linearised adjustment model for the affine, similarity and congruence transformations in 3D that is easily extendable with other parameters to describe deformations. The model considers all coordinates stochastic. Full positive semi-definite covariance matrices and correlation between epochs can be handled. The determination of transformation parameters between two or more coordinate sets, determined by geodetic monitoring measurements, can be handled as a least squares adjustment problem. It can be solved without linearisation of the functional model, if it concerns an affine, similarity or congruence transformation in one-, two- or three-dimensional space. If the functional model describes more than such a transformation, it is hardly ever possible to find a direct solution for the transformation parameters. Linearisation of the functional model and applying least squares formulas is then an appropriate mode of working. The adjustment model is given as a model of observation equations with constraints on the parameters. The starting point is the affine transformation, whose parameters are constrained to get the parameters of the similarity or congruence transformation. In this way the use of Euler angles is avoided. Because the model is linearised, iteration is necessary to get the final solution. In each iteration step approximate coordinates are necessary that fulfil the constraints. For the affine transformation it is easy to get approximate coordinates. For the similarity and congruence transformation the approximate coordinates have to comply to constraints. To achieve this, use is made of the singular value decomposition of the rotation matrix. To show the effectiveness of the proposed adjustment model total station measurements in two epochs of monitored buildings are analysed. Coordinate sets with full, rank deficient covariance matrices are determined from the measurements and adjusted with the proposed model. Testing the adjustment for deformations results in detection of the simulated deformations."
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When it comes to hard to solve problems, the significance of situational knowledge construction and network coordination must not be underrated. Professional deliberation is directed toward understanding, acting and analysis. We need smart and flexible ways to direct systems information from practice to network reflection, and to guide results from network consultation to practice. This article presents a case study proposal, as follow-up to a recent dissertation about online simulation gaming for youth care network exchange (Van Haaster, 2014).
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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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The Maritime Spatial Planning (MSP) Challenge simulation platform helps planners and stakeholders understand and manage the complexity of MSP. In the interactive simulation, different data layers covering an entire sea region can be viewed to make an assessment of the current status. Users can create scenarios for future uses of the marine space over a period of several decades. Changes in energy infrastructure, shipping, and the marine environment are then simulated, and the effects are visualized using indicators and heat maps. The platform is built with advanced game technology and uses aspects of role-play to create interactive sessions; it can thus be referred to as serious gaming. To calculate and visualize the effects of planning decisions on the marine ecology, we integrated the Ecopath with Ecosim (EwE) food web modeling approach into the platform. We demonstrate how EwE was connected to MSP, considering the range of constraints imposed by running scientific software in interactive serious gaming sessions while still providing cascading ecological feedback in response to planning actions. We explored the connection by adapting two published ecological models for use in MSP sessions. We conclude with lessons learned and identify future developments of the simulation platform.
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The design of healthcare facilities is a complex and dynamic process, which involves many stakeholders each with their own set of needs. In the context of healthcare facilities, this complexity exists at the intersection of technology and society because the very design of these buildings forces us to consider the technology–human interface directly in terms of living-space, ethics and social priorities. In order to grasp this complexity, current healthcare design models need mechanisms to help prioritize the needs of the stakeholders. Assistance in this process can be derived by incorporating elements of technology philosophy into existing design models. In this article, we develop and examine the Inclusive and Integrated Health Facilities Design model (In2Health Design model) and its foundations. This model brings together three existing approaches: (i) the International Classification of Functioning, Disability and Health, (ii) the Model of Integrated Building Design, and (iii) the ontology by Dooyeweerd. The model can be used to analyze the needs of the various stakeholders, in relationship to the required performances of a building as delivered by various building systems. The applicability of the In2Health Design model is illustrated by two case studies concerning (i) the evaluation of the indoor environment for older people with dementia and (ii) the design process of the redevelopment of an existing hospital for psychiatric patients.
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Acknowledging the knowledge gaps and novel paradigms outlined above within both coaching research and practice, the PhD research aims to investigate how need-supportive coaching—rooted in Self-Determination Theory (SDT)—effectively fulfils the satisfaction of beginning teachers’ basic psychological needs (BPNs) and autonomous motivation in pursuing their coaching goals within dyadic coaching relationships. To systematically explore this overarching objective, this PhD project and thesis adopt a structured, four-step approach, where each step investigates specific and complementary aspects of the coaching process. Collectively, these steps provide a comprehensive examination of how and under what conditions BPN-supportive coaching facilitates optimal motivational outcomes, enriching our understanding of the dynamic processes that shape coaching effectiveness among beginning teachers. Specifically, four research questions systematically guide the four research steps:1. What is the current state of empirical evidence linking BPN support, BPN satisfaction, and autonomous motivation within coach-coachee relationships?2. How do perceptual distances between coaches’ and coachees’ perceptions regarding BPN support relate to the coachees’ BPN satisfaction?3. How do coaches’ and coachees’ BPN satisfaction mutually predict autonomous motivation toward shared goals in their dyadic relationships?4. How do coaches and coachees dynamically co-regulate BPN interactions in real-time dyadic coaching conversations?Chapter 1 outlined a four-step empirical approach across Chapters 2 to 5 to understand BPN-supportive coaching. Chapter 2 meta-analysed the extant literature guided by a circular framework connecting BPN support, BPN satisfaction, and autonomous motivation for both parties. The results revealed that previous studies predominantly used individual-level data, neglecting the dynamic, reciprocal interactions in coaching. Chapter 3 investigated perceptual distance between coaches and coachees regarding BPN support using polynomial regression and response surface analysis. Results indicated that coachees reported greater BPN satisfaction when perceptions were closely aligned. Chapter 4 adopted the Actor-Partner Interdependence Model to examine how both parties’ BPN satisfaction is associated with their own and each other’s autonomous motivation. We found a positive unidirectional association between coachees’ relatedness satisfaction and coaches’ autonomous motivation and bidirectional associations for autonomy satisfaction. Chapter 5 employed State Space Grid analyses to capture the moment-to-moment co-regulation of autonomy in coach-coachee dyads. Results revealed recurrent patterns of predominant functional co-regulation (e.g., autonomy support met with proactive autonomy expression), and occasional dysfunctional co-regulation (e.g., evaluative feedback met with disengagement). Temporal evolvement in autonomy co-regulation was identified across coaching sessions in response to changing goals. Chapter 6 synthesised the contributions of the thesis. Collectively, BPN-supportive coaching can be viewed as a context-sensitive, interdependent, co-regulatory, and dynamic process, and we provided guidance for adaptive and relationally grounded coaching practices.
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