History education frequently aims at developing active citizenship by using the past to orientate to the present and the future. A pedagogy for pursuing this aim is making connections between the past and the present by means of comparing cases of an enduring human issue. To examine the feasibility and desirability of this case-comparison teaching approach, students (N = 444) and teachers (N = 15) who participated in an implementation study conducted in the Netherlands were questioned about their experiences and views. Results show that both students and teachers felt that case-comparison in the context of an enduring human issue is feasible and not more complex than the usual history teaching in which topics are studied separately without explicitly making comparisons between past and present, even if some students thought that taking account of episodes from different historical periods concurrently required an extra learning effort. Both students and teachers believed that connecting past and present in history teaching enhances engagement and meaning making. They suggested a curriculum combining the case-comparison approach with the type of history teaching they were accustomed to. Mixed methods were used for data collection. Implications for further research on case-comparison learning in history are being discussed.
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From the article: "The educational domain is momentarily witnessing the emergence of learning analytics – a form of data analytics within educational institutes. Implementation of learning analytics tools, however, is not a trivial process. This research-in-progress focuses on the experimental implementation of a learning analytics tool in the virtual learning environment and educational processes of a case organization – a major Dutch university of applied sciences. The experiment is performed in two phases: the first phase led to insights in the dynamics associated with implementing such tool in a practical setting. The second – yet to be conducted – phase will provide insights in the use of pedagogical interventions based on learning analytics. In the first phase, several technical issues emerged, as well as the need to include more data (sources) in order to get a more complete picture of actual learning behavior. Moreover, self-selection bias is identified as a potential threat to future learning analytics endeavors when data collection and analysis requires learners to opt in."
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poster voor de EuSoMII Annual Meeting in Pisa, Italië in oktober 2023. PURPOSE & LEARNING OBJECTIVE Artificial Intelligence (AI) technologies are gaining popularity for their ability to autonomously perform tasks and mimic human reasoning [1, 2]. Especially within the medical industry, the implementation of AI solutions has seen an increasing pace [3]. However, the field of radiology is not yet transformed with the promised value of AI, as knowledge on the effective use and implementation of AI is falling behind due to a number of causes: 1) Reactive/passive modes of learning are dominant 2) Existing developments are fragmented 3) Lack of expertise and differing perspectives 4) Lack of effective learning space Learning communities can help overcome these problems and address the complexities that come with human-technology configurations [4]. As the impact of a technology is dependent on its social management and implementation processes [5], our research question then becomes: How do we design, configure, and manage a Learning Community to maximize the impact of AI solutions in medicine?
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