ExposAR: Bringing Augmented Reality to the Computational Thinking Agenda through a Collaborative Authoring Tool

Lunding, Mille Skovhus; Grønbæk, Jens Emil; Bilstrup, Karl-Emil Kjær; Sørensen, Marie-Louise Stisen Kjerstein; Petersen, Marianne Graves

doi:10.1145/3491102.3517636

Cited by 10 publications

(1 citation statement)

References 34 publications

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“…Another way to facilitate the development of AR applications is by targeting specific domains. For instance, ZeusAR (Marín-Vega et al 2022) defines an architecture and a process to automate the generation of serious games based on AR, Ex-posAR (Lunding et al 2022) is an authoring tool for the creation of AR games by children, and there are also many proposals oriented to education, like VEDILS (Mota et al 2018) (see (Dengel et al 2022) for a recent survey). In contrast, our proposal is domain-agnostic so it can be applied to any domain.…”

Section: Ar Development Toolsmentioning

confidence: 99%

Model-Driven Engineering for Augmented Reality.

Campos-López¹,

Guerra²,

Lara³

et al. 2023

JOT

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The steady increase of the capabilities of mobile devices and the appearance of novel head-mounted widgets has triggered the interest in developing Augmented Reality (ARs) applications. In these applications, virtual objects can be overlaid over the real ones, and the user can interact with the virtual objects. AR applications open the door to innovative scenarios both for industrial use and leisure. However, their construction requires substantial effort and specialised knowledge.In this paper, we report on a model-driven approach to build AR applications that eliminates the need for manual coding. It is based on the definition of a domain meta-model, enriched with AR representations for the domain concepts and a description of the interaction with external information systems and devices. This paper presents the concepts, the technical realisation in an iOS tool called ALTER, an evaluation on five case studies, and a user study demonstrating its usefulness and usability.

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Section: Ar Development Toolsmentioning

confidence: 99%

Model-Driven Engineering for Augmented Reality.

Campos-López¹,

Guerra²,

Lara³

et al. 2023

JOT

View full text Add to dashboard Cite

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Co-design Pedagogy for Computational Thinking Education in K-12: A Systematic Literature Review

Sunday,

Agbo,

Suhonen

2024

Tech Know Learn

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The recent popularity of computational thinking (CT) and the desire to apply CT in our daily lives have prompted the need for a successful pedagogical technique for learning CT in K-12 education. The application of co-design pedagogical techniques has the potential to improve students’ CT learning through knowledge sharing and the creation of ideas to solve problems and develop an artifact. However, there is a limited understanding of how co-design pedagogical techniques have been explored to foster CT learning, which could hamper the successful use of co-design as a pragmatic teaching approach. This study examined the ways in which co-design pedagogical techniques have been applied in CT education by implementing a systematic literature review (PRISMA protocol) to document the review analysis. A total of 26 articles that met the inclusion criteria for this study were reviewed. Findings in this study revealed that workshops are the most utilized co-design learning setting and, as expected, the collaborative technique is the co-design pedagogical technique most frequently adopted for implementing CT in K-12 education. NetLogo is the most frequently used co-design tool for teaching and learning CT in K-12 education, and an interdependence exists between NetLogo and the Common Online Data Analysis Platform. Co-design also helps teachers develop the ability to use co-design pedagogical techniques to learn, create content, and integrate CT into their various subjects. This study contributes to practical knowledge by unraveling and advocating the use of dialogical, prompting, framing, and game-based techniques as co-design pedagogical techniques for K-12 teachers and also helps teachers identify useful co-design tools for learning CT.

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