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Facilitating Geometry Learning Through Real-Time Collaborative Activities with Augmented Reality in Authentic Context

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Innovative Technologies and Learning (ICITL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13449))

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Abstract

In this study, a smart Ubiquitous Geometry (Smart-UG) system with smart mechanisms support was developed to facilitate learners’ geometry learning through measuring objects in authentic context. Further, we designed a real-time collaborative measuring function based on the augmented reality (AR) in the mobile device. Learners were required to measure objects collaboratively with their peers, calculate the surface area and perimeter of the objects in authentic contexts, and discuss. In order to facilitate meaningful collaboration, interaction and socialization among peers, we designed the smart mechanisms that provided learners with a way to ask others for help if they face difficulty. Participants were fifth-grade learners from two classes that were assigned into different groups. The experimental group (EG) consists of 26 learners and the control group (CG) consists of 26 learners with/without smart mechanisms support, respectively. The results revealed that EG with assistance of smart mechanisms like peer help significantly improved geometry ability and got higher scores than CG. In addition, EG who completed more practices, made fewer mistakes, and asked others for help had a better understanding of geometry concepts. Moreover, learners showed a positive attitude and high intention toward Smart-UG. Therefore, our Smart-UG with the smart mechanism support could facilitate learners to learn geometry smartly in authentic context and enhance their geometry ability.

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Correspondence to Wu-Yuin Hwang .

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Hwang, WY., Lin, YJ., Hoang, A., Nurtantyana, R., Lin, O. (2022). Facilitating Geometry Learning Through Real-Time Collaborative Activities with Augmented Reality in Authentic Context. In: Huang, YM., Cheng, SC., Barroso, J., Sandnes, F.E. (eds) Innovative Technologies and Learning. ICITL 2022. Lecture Notes in Computer Science, vol 13449. Springer, Cham. https://doi.org/10.1007/978-3-031-15273-3_9

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  • DOI: https://doi.org/10.1007/978-3-031-15273-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15272-6

  • Online ISBN: 978-3-031-15273-3

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