We developed an application which allows learners to construct qualitative representations of dynamic systems to aid them in learning subject content knowledge and system thinking skills simultaneously. Within this application, we implemented a lightweight support function which automatically generates help from a norm-representation to aid learners as they construct these qualitative representations. This support can be expected to improve learning. Using this function it is not necessary to define in advance possible errors that learners may make and the subsequent feedback. Also, no data from (previous) learners is required. Such a lightweight support function is ideal for situations where lessons are designed for a wide variety of topics for small groups of learners. Here, we report on the use and impact of this support function in two lessons: Star Formation and Neolithic Age. A total of 63 ninth-grade learners from secondary school participated. The study used a pretest/intervention/post-test design with two conditions (no support vs. support) for both lessons. Learners with access to the support create better representations, learn more subject content knowledge, and improve their system thinking skills. Learners use the support throughout the lessons, more often than they would use support from the teacher. We also found no evidence for misuse, i.e., 'gaming the system', of the support function.
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What makes an educational game good? This paper describes three research directions that could provide insight in the underlying principles of effective educational games. These aspects are 1) The importance of distinguishing between types of to-be-learned knowledge, 2) the need to understand the relationship between game mechanics and learning goals, and 3) using research on intelligent tutoring systems to create more personalized learning experiences. Central in these directions is the concept of cognition and how it impacts the educational effectiveness of an educational game. This paper will give a short introduction on cognition and discuss why the research directions require further research.
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Om goed in te spelen op complexe ontwikkelingen in onze snel veranderende samenleving, hebben organisaties hulp en handvatten nodig. In dit artikel wordt daartoe een driestapsaanpak beschreven voor het inzetten van toekomstscenario’s bij strategievorming: identificeren van drivers for change; toekomstscenario’s creëren en toekomstscenario’s toepassen. Deze aanpak is in de praktijk toegepast, drie van deze praktijkcases worden hier beschreven: ‘Grafimedia 3.0’; ‘The food after tomorrow’ en ‘Een leefbare stad Eindhoven 2030’. Op basis van de resultaten uit deze cases en de verschillen en overeenkomsten ertussen kunnen de volgende aanbevelingen worden gegeven: de tijdshorizon van de toekomstscenario’s moet aansluiten bij de urgentie van de opdrachtgever; adequate begeleiding bij het toepassen van de toekomstscenario’s is onmisbaar en het is belangrijk om actief te zijn in het verbeelden van de toekomst, voor zowel de makers van de toekomstscenario’s als voor de opdrachtgever. In order to comply with the complex and rapid developments of our society when directing an organization, policymakers need help and guidance. To offer these, this article describes a three-step approach for using future scenarios in strategy development: the identification of drivers for change, the creation of future scenarios and the practical application of these future scenarios. This approach has been used in real life and three of these cases are described here: ‘Grafimedia 3.0’; ‘The food after tomorrow’ and ‘Een leefbare stad Eindhoven 2030’ (‘A livable Eindhoven in 2030’). The following recommendations can be derived based on the case results and their differences and similarities: the time scale of the future scenarios should match the urgency of the client’s situation, adequate tutoring in the practical application of the future scenarios is essential and, lastly, it is important, not only for the creators of the future scenarios but also for the client, to imagine the future in an active fashion.
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Robot tutors provide new opportunities for education. However, they also introduce moral challenges. This study reports a systematic literature re-view (N = 256) aimed at identifying the moral considerations related to ro-bots in education. While our findings suggest that robot tutors hold great potential for improving education, there are multiple values of both (special needs) children and teachers that are impacted (positively and negatively) by its introduction. Positive values related to robot tutors are: psychological welfare and happiness, efficiency, freedom from bias and usability. However, there are also concerns that robot tutors may negatively impact these same values. Other concerns relate to the values of friendship and attachment, human contact, deception and trust, privacy, security, safety and accountability. All these values relate to children and teachers. The moral values of other stakeholder groups, such as parents, are overlooked in the existing literature. The results suggest that, while there is a potential for ap-plying robot tutors in a morally justified way, there are imported stake-holder groups that need to be consulted to also take their moral values into consideration by implementing tutor robots in an educational setting. (from Narcis.nl)
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This report is the final report for the FPGA accelerated PID controller, part of the Distributed Control Systems project. This project runs within the Lectoraat Robotics and High Tech Mechatronics of Fontys Hogeschool Engineering Eindhoven. The Lectoraat has the goal to develop applicable knowledge to support education and industry. This knowledge is acquired with projects run in conjunction with the industry. The report will go into detail for the software designed for this project, not the hardware design. This report is intended for follow up students working on the Distributed Control Systems project. Within this report the assumption is made that the reader is at least familiar with the terms EtherCAT, FPGA, Linux and PID controllers. However for each part a small basic introduction is included. For readers looking for the accomplishments in this project, the results are in chapter six. Following are short descriptions of the chapters in this report. The first chapter will give a short introduction to the project. It talks about why the project was conceived, where the project was done and what the expected end result is. The second chapter, the problem definition, talks about how the project has been defined, what is included and what is not and how the customer expects the final product to function and look like. The third chapter details the methodology used during this project. All the research preformed for this project will be described in the forth chapter. This chapter goes into the research into the Xilinx Zynq 7000 chip, Beckhoff's EtherCAT system, how the Serial Peripheral Interface works and how a PID controller functions. Following in chapter five the design is expanded upon. First the toolchain for building for the Zynq chip is explained. This is followed by and explanation of the different software parts that have been designed. Finally chapters six and seven provide the results and the conclusions and recommendations for this project.
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Technological developments have a major impact on how we live, work and learn together. Several authors refer to a fourth revolution in which robots and other intelligent systems take over an increasing number of the current (routine) tasks carried out by humans (Brynjolfsson & McAfee, 2014; Est et al., 2015; Ford, 2016; Helbing, 2014; Ross, 2017; Schwab, 2016). The relationship between man and machine will change fundamentally as a result. We are already noticing this shift, most specifically in the workplace. E.g., in the field of health care, digitalisation and robotisation can empower patients and their families. Hospitals are primarily intended for clients with complex care needs. This has consequences for the tasks carried out by nurses, who become more of a ‘care director’ or ‘research nurse’. Hospitals approach this in different ways, resulting in considerable diversity as to how these roles are fulfilled. These changes, albeit diverse, can also be seen in the roles of accountants, police officers and financial advisers at banks (Biemans, Sjoer, Brouwer and Potting, 2017). The traditional occupational profiles no longer exist and the essence of these professions is shifting. This does not make such occupations less attractive, but requires different qualities. The demand for more highly educated professionals who can carry out complex tasks in a creative and interdisciplinary manner will increase (McKinsey, 2017). Also, other social developments, such as migration and greenification, prompt us to ask new questions, resulting in new paths towards identifying solutions.
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In this short article the author reflects on AI’s role in education by posing three questions about its application: choosing a partner, grading assignments, and replacing teachers. These questions prompt discussions on AI’s objectivity versus human emotional depth and creativity. The author argues that AI won’t replace teachers but will enhance those who embrace its potential while understanding its limits. True education, the author asserts, is about inspiring renewal and creativity, not merely transmitting knowledge, and cautions against letting AI define humanity’s future.
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Learning by creating qualitative representations is a valuable approach to learning. However, modelling is challenging for students, especially in secondary education. Support is needed to make this approach effective. To address this issue, we explore automated support provided to students while they create their qualitative representation. This support is generated form a reference model that functions as a norm. However, the construction of a reference models is still a challenge. In this paper, we present the reference model that we have created to support students in learning about the melatonin regulation in the context of the biological clock.
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One of the claims the OER movement makes is that availability of (open) digital learning materials improves the quality of education. The promise is the ability to offer educational programs that take into account specific demands of the learner. The question is how to reach a situation where a customized demand can be met using OER with acceptable quality against acceptable costs. This situation resembles mass customization as is common in industry for several decades now. Techniques from an industry where an end product is assembled with the demands of the customer as a starting point can be translated to the field of education where courses and learning paths through a curriculum are assembled using a mixture of open and closed learning materials and learning services offered by an institution. Advanced IT support for both the modeling of the learning materials and services and a configurator to be used by a learner are necessary conditions for this approach.
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This study investigated to what degree lesson-to-lesson variability in teachers' goal clarification and process feedback explains variability in secondary students’ motivational correlates. Students (N=570, 24 classes) completed questionnaires at six occasions. Multilevel regression analyses showed that relations between perceived process feedback and experienced need satisfaction (i.e., competence, autonomy and relatedness) were conditional on perceived goal clarification. No such interaction effects between process feedback and goal clarification were found for need frustration (i.e., experiencing failure, feeling pushed to achieve goals, feeling rejected). In general, when students perceived more process feedback or goal clarification, students experienced more competence, autonomy and relatedness satisfaction. Yet, when perceiving very high levels of process feedback, additional benefits of goal clarification were no longer present (and vice versa). In lessons in which students perceived goals to be less clear, they experienced more need frustration. No associations were found between process feedback and need frustration.
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