The rapid growth of engineering education as a field of rigorous research has resulted in an explosion of available data and research results. There are numerous research efforts currently underway that gather data on a variety of topics that have the potential to help us better understand how students learn engineering. However, there are currently no easy methods to synthesize research results, share research data, and indeed validate research studies effectively. In general, topics related to data and data sharing are largely treated as taboos in the engineering education research space. Data sharing mechanisms to enable fundamental research in engineering education that has the potential to address systemic problems have not yet been clarified. The research goal of this paper is to identify and understand patterns for data sharing mechanisms in order to inform design requirements for data sharing practices and infrastructure in engineering education.
This article describes a novel two‐step approach of detecting and understanding dis/misinformation events in social media that occur during disasters and crisis events. To detect false news events, we designed a deep learning‐based detection algorithm and then trained it with a transfer learning scheme so that the algorithm could decide whether a given group of rumor‐related tweets is a dis/misinformation event. For understanding how dis/misinformation was diffused in social networks and identifying those who are responsible for creating and consuming false information, we present DismisInfoVis, which consists of various visualisations, including a social network graph, a map, line charts, pie charts, and bar charts. By integrating these deep learning and multi‐view visualisation techniques, we could gain a deeper insight into dis/misinformation events in social media from multiple angles. We describe in detail the implementation, training process, and performance evaluations of the detection algorithm and the design and utilization of DismisInfoVis for dis/misinformation data analyses. We hope that this study will contribute to improving the quality of information generated and shared on social media during critical times, eventually helping both the affected and the general public recover from the impacts of disasters and crisis events.
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