Formative assessment (FA) is an effective educational approach for optimising student learning and is considered as a promising avenue for assessment within physical education (PE). Nevertheless, implementing FA is a complex and demanding task for in-service PE teachers who often lack formal training on this topic. To better support PE teachers in implementing FA into their practice, we need better insight into teachers’ experiences while designing and implementing formative strategies. However, knowledge on this topic is limited, especially within PE. Therefore, this study examined the experiences of 15 PE teachers who participated in an 18-month professional development programme. Teachers designed and implemented various formative activities within their PE lessons, while experiences were investigated through logbook entries and focus groups. Findings indicated various positive experiences, such as increased transparency in learning outcomes and success criteria for students as well as increased student involvement, but also revealed complexities, such as shifting teacher roles and insufficient feedback literacy among students. Overall, the findings of this study underscore the importance of a sustained, collaborative, and supported approach to implementing FA.
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This paper addresses an approach to teaching embedded systems programming through a challenge-based competition involving robots. This pedagogical project distinguishes itself by incorporating international students from three international institutions through the Blended Intensive Program (BIP). The research findings indicate that this approach yields excellent results regarding student engagement and learning outcomes. The challenge-based program effectively promotes students' creative problem-solving abilities by combining theoretical instruction with hands-on experience in a competitive setting.
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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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