1,696 research outputs found
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Exploring the geographic uncertainty associated with crowdsourced crisis information: a geovisualisation approach
New information and communications technologies, such as mobile phones and social media, have presented the humanitarian community with a dilemma: how should humanitarian organisations integrate information from crisis-affected communities into their decision-making processes whilst guarding against inaccurate information from untrustworthy sources? Advocates of crisis mapping claim that, under certain circumstances, crowdsourcing can increase the accuracy of crisis information. However, whilst previous research has studied the geography of crisis information, the motivations of people who create crisis map mashups, and the motivations of people who crowdsource crisis information, the geography of, and the uncertainty associated with, crowdsourced crisis information has been ignored. As such, the current research is motivated by the desire to explore the geographic uncertainty associated with, and to contribute a better understanding of, crowdsourced crisis information.
The current research contributes to the fields of GISc (Geographic Information Science) and crisis informatics; crisis mapping; and geovisualisation specifically and information visualisation more generally. These contributions can be summarised as an approach to, and an understanding of, the geographic uncertainty associated with crowdsourced crisis information; three geovisualisation software prototypes that can be used to identify meaningful patterns in crisis information; and the design, analysis, and evaluation model, which situates the activities associated with designing a software artefact-and using it to undertake analysis-within an evaluative framework. The approach to the geographic uncertainty associated with crowdsourced crisis information synthesised techniques from GISc, geovisualisation, and natural language processing. By following this approach, it was found that location descriptions from the Haiti crisis map did not 'fit' an existing conceptual model, and, consequently, that there is a need for new or enhanced georeferencing methods that attempt to estimate the uncertainty associated with free-text location descriptions from sources of crowdsourced crisis information
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Understanding Emotions in Online Learning: Using Emotional Design and Emotional Measurement to Unpack Complex Emotions During Collaborative Learning
Many educational researchers explore the role of emotion in learning and there are many new affordances for emotional measurement. Just as there are many options for emotional measurement there are many theories of emotion. When it comes to the measure of sentiment analysis recent findings suggest it is beneficial to online and blended learning research. The sentiment analysis technologies used for educational research are general purpose technologies suggesting that creating a measure designed for the context of learning would improve the alignment between the measure and context. In addition to aligning measure with the context, there is a need to consider how sentiment analysis relates to emotion theory to determine an appropriate method to evaluate the accuracy of sentiment analysis. In this PhD thesis I adopt the Constructed Theory of Emotion, which considers emotion as a collective intentionality indicating that consensus on emotion is the best approach toward examining accuracy. From this perspective I create a sentiment analysis measure in the context of learning to contribute to emotional learning analytics the emerging sub-field of learning analytics. The field of learning analytics acknowledges that design and measurement are intertwined. I adopt a design-based research approach by designing supports for emotional communication and examining how such a design impacts the accuracy of sentiment analysis. I then examine correlation analysis with other established measures of emotion. The results contribute to the field of emotional learning analytics by:
• demonstrating promise for generating a classifier based on student perception
• demonstrating benefits of supporting emotion expression in text for students
• demonstrating that students’ emotion expression in text does not appear to align with their internal emotional experiences
These findings provide opportunities for further research and suggest caution should be used when interpreting sentiment analysis results in the context of learning
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