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Register a new userCurious Minds Wonder Alike: Studying Multimodal Behavioral Dynamics to Design Social Scaffolding of Curiosity
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Added by
Tanmay Sinha
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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Curiosity is the strong desire to learn or know more about something or someone. Since learning is often a social endeavor, social dynamics in collaborative learning may inevitably influence curiosity. There is a scarcity of research, however, focusing on how curiosity can be evoked in group learning contexts. Inspired by a recently proposed theoretical framework that articulates an integrated socio-cognitive infrastructure of curiosity, in this work, we use data-driven approaches to identify fine-grained social scaffolding of curiosity in child-child interaction, and propose how they can be used to elicit and maintain curiosity in technology-enhanced learning environments. For example, we discovered sequential patterns of multimodal behaviors across group members and we describe those that maximize an individuals utility, or likelihood, of demonstrating curiosity during open-ended problem-solving in group work. We also discovered, and describe here, behaviors that directly or in a mediated manner cause curiosity related conversational behaviors in the interaction, with twice as many interpersonal causal influences compared to intrapersonal ones. We explain how these findings form a solid foundation for developing curiosity-increasing learning technologies or even assisting a human coach to induce curiosity among learners.
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Learning behavior mechanism is widely anticipated in managed settings through the formal syllabus. However, heading for learning stimulus whilst daily mobility practices through urban transit is the novel feature in learning sciences. Theory of planned behavior (TPB), technology acceptance model (TAM), and service quality of transit are conceptualized to assess the learning behavioral intention (LBI) of commuters in Greater Kuala Lumpur. An online survey was conducted to understand the LBI of 117 travelers who use the technology to engage in the informal learning process during daily commuting. The results explored that all the model variables i.e., perceived ease of use, perceived usefulness, service quality, and subjective norms are significant predictors of LBI. The perceived usefulness of learning during traveling and transit service quality has a vibrant impact on LBI. The research will support the informal learning mechanism from commuters point of view. The study is a novel contribution to transport and learning literature that will open the new prospect of research in urban mobility and its connotation with personal learning and development.
Curiosity is a vital metacognitive skill in educational contexts. Yet, little is known about how social factors influence curiosity in group work. We argue that curiosity is evoked not only through individual, but also interpersonal activities, and present what we believe to be the first theoretical framework that articulates an integrated socio-cognitive account of curiosity based on literature spanning psychology, learning sciences and group dynamics, along with empirical observation of small-group science activity in an informal learning environment. We make a bipartite distinction between individual and interpersonal functions that contribute to curiosity, and multimodal behaviors that fulfill these functions. We validate the proposed framework by leveraging a longitudinal latent variable modeling approach. Findings confirm positive predictive relationship of the latent variables of individual and interpersonal functions on curiosity, with the interpersonal functions exercising a comparatively stronger influence. Prominent behavioral realizations of these functions are also discovered in a data-driven way. This framework is a step towards designing learning technologies that can recognize and evoke curiosity during learning in social contexts.
This paper explores a design study of a smartphone enabled meet-up app meant to inspire engagement in community innovation. Community hubs such as co-working spaces, incubators, and maker spaces attract community members with diverse interests. This paper presents these spaces as a design opportunity for an application that helps host community-centered meet-ups in smart and connected communities. Our design study explores three scenarios of use, inspired by previous literature, for organizing meet-ups and compares them by surveying potential users. Based on the results of our survey, we propose several design implications and implement them in the Community Animator geosocial networking application, which identifies nearby individuals that are willing to chat or perform community-centered activities. We present the results of both our survey and our prototype, discuss our design goals, and provide design implications for civic-minded, geosocial networking applications. Our contribution in this work is the development process, proposed design of a mobile application to support community-centered meet-ups, and insights for future work.
Research in machine learning (ML) has primarily argued that models trained on incomplete or biased datasets can lead to discriminatory outputs. In this commentary, we propose moving the research focus beyond bias-oriented framings by adopting a power-aware perspective to study up ML datasets. This means accounting for historical inequities, labor conditions, and epistemological standpoints inscribed in data. We draw on HCI and CSCW work to support our argument, critically analyze previous research, and point at two co-existing lines of work within our community -- one bias-oriented, the other power-aware. This way, we highlight the need for dialogue and cooperation in three areas: data quality, data work, and data documentation. In the first area, we argue that reducing societal problems to bias misses the context-based nature of data. In the second one, we highlight the corporate forces and market imperatives involved in the labor of data workers that subsequently shape ML datasets. Finally, we propose expanding current transparency-oriented efforts in dataset documentation to reflect the social contexts of data design and production.
Multimedia content in social media platforms provides significant information during disaster events. The types of information shared include reports of injured or deceased people, infrastructure damage, and missing or found people, among others. Although many studies have shown the usefulness of both text and image content for disaster response purposes, the research has been mostly focused on analyzing only the text modality in the past. In this paper, we propose to use both text and image modalities of social media data to learn a joint representation using state-of-the-art deep learning techniques. Specifically, we utilize convolutional neural networks to define a multimodal deep learning architecture with a modality-agnostic shared representation. Extensive experiments on real-world disaster datasets show that the proposed multimodal architecture yields better performance than models trained using a single modality (e.g., either text or image).
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