Smart cities try to use technology and data to improve the efficiency of city services, to address societal challenges and to improve collaboration between citizens and government. Driving such social change requires a deep socio-ecological transition with both organisations and individual's lifestyles needing to change. This has led to an increasing recognition of the need for education to empower people to play a more active role within smart city initiatives. This paper examines our use of a MOOC as a tool to facilitate attitudinal learning around the topic of smart cities and to explore whether the learners engage in local smart city activities. A mixed methods research design was employed, collecting data via an online survey that was completed by 202 learners and through in-depth interviews with 8 of those learners. The results show that learners’ perceived high levels of attitudinal learning on the topic of smart cities across four categories of learning outcomes (general, cognitive, affective and behavioural). Our findings also contribute to an understanding of the types of post-course activities learners participate in and their experiences of trying to apply what they learnt if they participated in local smart city activities. We conclude by providing new insights into how to design for learning to support social change in the context of a MOOC.

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This workshop is concerned with the potentials and barriers of data in facilitating bottom-up innovation. We focus on two specific communities that could benefit from utilizing data, but may struggle currently. Firstly, civil society participating in grassroots actions, who use public data for innovation and to strengthen their advocacy. Secondly, organisations - especially SME's - who want to make use of data, especially large complex data sets, to design data-driven services.@en

The Internet of Things (IoT), a type of cyber-physical system, has led to a drastic growth in the number of devices and sensors connected to each other and to the digital word. This has further led to an exponential increase in the amount of data being produced and disseminated throughout such systems. This data has the potential to provide valuable insights into user behavior that can inform a design process. It also comprises an important aspect of an IoT product or service that an end user might interact to gain actionable insights. For example, when to use energy in the home, how to avoid polluted or flooded areas, or to visit the shops at quiet times. These same users may also be one source of the data that is analysed to provide this intelligence. However, in many case more intelligence is gained by combining different data sets. This raises questions about how to help both designers and end-users to get the most value from the insights acquired through the combination and analysis of IoT data, whilst being sensitive to issues around privacy and security of data contributed by the public. There is currently no clear framework to support designers in navigating through a design process that uses and combines such complex data. The aim of this one day workshop is to explore how to effectively incorporate data into a design process and how to design for more effective interactions between humans and data within IoT technologies. It will also create a roadmap for development of new methods and tools to support responsible, data-driven, co-design of new IoT interactive products and services.

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