This research investigates the potential and challenges of using artificial intelligence, specifically the ChatGPT-4 model developed by OpenAI, in grading and providing feedback in an educational setting. By comparing the grading of a human lecturer and ChatGPT-4 in an experiment with 105 students, our study found a strong positive correlation between the scores given by both, despite some mismatches. In addition, we observed that ChatGPT-4's feedback was effectively personalized and understandable for students, contributing to their learning experience. While our findings suggest that AI technologies like ChatGPT-4 can significantly speed up the grading process and enhance feedback provision, the implementation of these systems should be thoughtfully considered. With further research and development, AI can potentially become a valuable tool to support teaching and learning in education. https://saiconference.com/FICC
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This experimental study with a pre-post and follow-up design evaluates the financial education program “SaveWise” for ninth grade students in the Netherlands (n = 713). SaveWise adopts a holistic approach, emphasizing action rather than mere cognition. Benefitting from explicit instruction embedded in real-life contexts, students in the program set a personal savings goal and are coached on how to achieve it. The short-term treatment results indicated that SaveWise expanded the students’ level of financial knowledge; encouraged their intentions to save more, spend less and earn an income; and broadly improved their financial and savings behavior. The program demonstrated that it could serve as an effective and low-cost method to enhance the financial literacy of pre-vocational students, a financially vulnerable group. Although long-term effects were expressed only through financial socialization, this study offers evidence linking curricula to increased knowledge and improved behavior for a specific sample of students.
MULTIFILE
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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The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
A transition to a circular economy is needed to revolutionize the construction sector and make it more sustainable for present and future generations. While the construction industry and the production of construction materials contribute to environmental pollution, they also offer great potential for addressing many environmental problems. Sheet materials are engineered wood boards that are produced from recycled or solid wood where an adhesive is used to bind the particles together, predominantly used in: Furniture manufacturing, Flooring application, Roofing, Wall sheathing. The most common binder for boards is urea-formaldehyde. Other binders may be used depending on the grade of board and its intended end-use. For example, melamine urea-formaldehyde, phenolic resins and polymeric diphenylmethane diisocyanate (PMDI) are generally used in boards that require improved moisture resistance. Formaldehyde is classified in the in the European Union as a carcinogen and it carries the hazard statement 'suspected of causing cancer'. In this project mycelium composites are developed as a formaldehyde-free, fully natural and biodegradable material with high potential to substitute these hazardous materials. The heat-press process, the feasibility of which was evaluated in a previous Kiem HBO project, is to be further developed towards a process where mycelium sheets with different thicknesses will be obtained. This is considered as a fundamental step to increase the material approachability to the market. Different Material manufacturing techniques are also considered to enable the increase of sample thicknesses and volume. Moreover, a business study will be incorporated to allow further understanding of the material market potential. The consortium composition of V8 Architects, QbiQ, Fairm, Verbruggen Paddestoelen BV, and CoEBBE merges different expertise and guarantees the consideration of the whole material production chain. The research will contribute to bring mycelium composites a step closer to the market, giving them visibility and increasing the possibility to a commercial breakthrough.
