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Previous research has documented the benefits of making for young learners, but few studies have examined how parents engage in maker activities during family visits to museums, both as facilitators of their children’s learning and as makers in their own right. In this study, we asked how caregivers participate in making and tinkering programs, how parents describe the benefits of making (for their children and themselves), and what aspects of the physical and social setting influence parents’ engagement. Data included observations of 88 family groups participating in various making and tinkering activities at a science center (including woodworking, fashion design, virtual reality drawing, circuit blocks, etc) and exit interviews with a subset of 66 caregivers. Qualitative data analysis connected observed qualities of the physical and social setting with caregivers’ observed and reported engagement. Through this analysis, we identified specific aspects of the physical environment, tools/materials, and facilitation strategies that invited family participation in general and that were associated with specific caregiver roles, including observing children’s learning, facilitation of children’s learning, and engagement as a maker alongside children. The implications of the findings for the design and facilitation of maker programs are discussed.
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AI Adaptivity in a Mixed-Reality System Improves Learning
Abstract Adaptivity in advanced learning technologies offer the possibility to adapt to different student backgrounds, which is difficult to do in a traditional classroom setting. However, there are mixed results on the effectiveness of adaptivity based on different implementations and contexts. In this paper, we introduce AI adaptivity in the context of a new genre of Intelligent Science Stations that bring intelligent tutoring into the physical world. Intelligent Science Stations are mixed-reality systems that bridge the physical and virtual worlds to improve children’s inquiry-based STEM learning. Automated reactive guidance is made possible by a specialized AI computer vision algorithm, providing personalized interactive feedback to children as they experiment and make discoveries in their physical environment. We report on a randomized controlled experiment where we compare learning outcomes of children interacting with the Intelligent Science Station that has task-loop adaptivity incorporated, compared to another version that provides tasks randomly without adaptivity. Our results show that adaptivity using Bayesian Knowledge Tracing in the context of a mixed-reality system leads to better learning of scientific principles, without sacrificing enjoyment. These results demonstrate benefits of adaptivity in a mixed-reality setting to improve children’s science learning.
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- PAR ID:
- 10485304
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- International Journal of Artificial Intelligence in Education
- Volume:
- 34
- Issue:
- 4
- ISSN:
- 1560-4292
- Format(s):
- Medium: X Size: p. 1541-1558
- Size(s):
- p. 1541-1558
- Sponsoring Org:
- National Science Foundation
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