In mathematics, sciences and economics, understanding and working with graphs are important skills. However, developing these skills has been shown to be a challenge in secondary and higher education as it involves high order thinking processes such as analysis, reflection and creativity. In this study, we present Interactive Virtual Math, a tool that supports the learning of a specific kind of graphs: dynamic graphs which represent the relation between at least two quantities that covary. The tool supports learners in visualizing abstract relations through enabling them to draw, move and modify graphs, and by combining graphs with other representations, especially interactive animations and textual explanations. This paper reports a design experiment about students’ learning graphs with this tool. Results show that students with difficulty in generating acceptable graphs improve their ability while working with the tool.
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In response to globalisation and internationalisation of both higher education and the job market, The Hague University of Applied Sciences (THUAS) has seen a considerable increase in English-medium courses, i.e. non-language subjects taught through English. Internationally, the rise of English-medium instruction (EMI) has led to research on, and discussion about the possible side-effects of a change in instructional language. More specifically, whether using a foreign language as the medium of instruction has a negative impact on teaching and learning. This paper reports the findings of a pilot research project into the implications of English-medium instruction (EMI) as perceived by students and teachers of the bachelor program Commercial Economics at the Faculty of Business, Finance and Administration at THUAS. Research methods used to collect data include face-to-face interviews with both students and lecturers involved in EMI subject courses, a student questionnaire, and lesson observations. Despite regular exposure to English and an adequate self-perceived English proficiency, results show that a considerable number of students, as well as teaching staff are experiencing difficulties with English-medium instruction and that for many EMI is not as efficient in transferring academic content as instruction in the mother tongue.
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Evaluation of the effect of Problem Based Learning course
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Presentation at the ALM28 Conference: Numeracy and Vulnerability, 5-7 july, Universität Hamburg, Germany.
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From the article: Abstract—By using agent technology, a versatile and modular monitoring system can be built. In this paper, such a multiagentbased monitoring system will be described. The system can be trained to detect several conditions in combination and react accordingly. Because of the distributed nature of the system, the concept can be used in many situations, especially when combinations of different sensor inputs are used. Another advantage of the approach presented in this paper is the fact that every monitoring system can be adapted to specific situations. As a case-study, a health monitoring system will be presented.
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Conference proceedings International Symposium on Intelligent Manufacturing Environments
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ion of verb agreement by hearing learners of a sign language. During a 2-year period, 14 novel learners of Sign Language of the Netherlands (NGT) with a spoken language background performed an elicitation task 15 times. Seven deaf native signers and NGT teachers performed the same task to serve as a benchmark group. The results obtained show that for some learners, the verb agreement system of NGT was difficult to master, despite numerous examples in the input. As compared to the benchmark group, learners tended to omit agreement markers on verbs that could be modified, did not always correctly use established locations associated with discourse referents, and made characteristic errors with respect to properties that are important in the expression of agreement (movement and orientation). The outcomes of the study are of value to practitioners in the field, as they are informative with regard to the nature of the learning process during the first stages of learning a sign language.
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One of the core elements of the Programme for the International Assessment of Adult Competencies (PIAAC) is a survey of adult skills. The survey measures adults’ proficiency in literacy, numeracy, and problem solving in technology-rich environments. Furthermore, the survey gathers data on how adults use their skills at home, at work and in the wider community. The second cycle of PIAAC will take place in 2021 and 2022. Preparations have started by the Numeracy Expert Group in reviewing the numeracy framework used in the first cycle and designing items which will be used to measure the numerate capabilities and numerate behavior of adults.
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We are well into the 21st century now and the urgency for lifelong learning is growing especially regarding numeracy. There are major societal and policy pressures on education to prepare citizens for a complex and technologized society, in literature referred to as “21st century skills” (Voogt & ParejaRoblin, 2012), “global competences” (OECD, 2016a) or “the 4th industrial revolution” (Schwab, 2016). International research has demonstrated the economic and social value of literacy and numeracy knowledge and skills (Hanushek and Wöbmann, 2012; Grotlüschen, et al. 2016). With respect to numeracy (and/or mathematics) education, we explore the implications of these pressures to the mathematical demands at individuals living and working in modern life, and what is expected from numeracy education as society moves further into the 21st century. New means of communication and types of services have changed the way individuals interact with governments, institutions, services and each other, and social and economic transformations have in turn, changed the nature of the demand for skills as well.
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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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