Learning through exploration is assumed to be a powerful way of introducing children to computer science concepts. However, it is uncertain how exploring physical computing toolkits can promote movement between conceptual knowledge and abstract reflection, and lead to critical thinking about technology. We investigated how children aged 9-11 years explored and reasoned about personal and environmental data sensors, using a playful explorationbased physical toolkit in their classroom. We report on the ways in which critical thinking about sensor accuracy and reliability developed through reflective dialogue and playful interaction, taking into account the support structures embedded in the classroom. Finally, we discuss strategies for designing exploration-based learning for classroom settings, to promote critical thinking about data sensing.

How can digitised assets of Galleries, Libraries, Archives and Museums be reused to unlock new value? What are the implications of viewing large-scale cultural heritage data as an economic resource, to build new products and services upon? Drawing upon valuation studies, we reflect on both the theory and practicalities of using mass-digitised heritage content as an economic driver, stressing the need to consider the complexity of commercial-based outcomes within the context of cultural and creative industries. However, we also problematise the act of considering such heritage content as a resource to be exploited for economic growth, in order to inform how we consider, develop, deliver and value mass-digitisation. Our research will be of interest to those wishing to understand a rapidly changing research and innovation landscape, those considering how to engage memory institutions in data-driven activities and those critically evaluating years of mass-digitisation across the heritage sector.

As with many industries, TV and video production is likely to be transformed by artificial intelligence (AI) and machine learning (ML), with software and algorithms assisting production tasks that, conventionally, could only be carried out by people. Expanded coverage of a diverse range of live events is particularly constrained by the relative scarcity of skilled people, and it is a strong use case for AI-based automation. This article describes the recent research conducted by the British Broadcasting Corporation (BBC) on the potential production benefits of AI algorithms, using visual analysis and other techniques. Rigging small, static ultrahigh-definition (UHD) cameras, we have enabled a one-person crew to crop UHD footage in multiple ways and cut between the resulting shots, effectively creating multicamera HD coverage of events that cannot accommodate a camera crew. By working with programmakers to develop simple deterministic rules and, increasingly, training systems using advanced video analysis, we are developing a system of algorithms to automatically frame, sequence, and select shots, and construct acceptable multicamera coverage of previously untelevised types of events.

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