Framework of Affordances for Virtual Reality and Augmented Reality

Steffen, Jacob; Gaskin, James; Meservy, Thomas O.; Jenkins, Jeffrey L.; Wolman, Iopa

doi:10.1080/07421222.2019.1628877

Cited by 187 publications

(130 citation statements)

References 36 publications

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“…They also give us a means of comparison when evaluating AR against physical reality, such as using a wet lab 39 or organ cross-section. 18 Importantly, there is no single set of affordances for AR in education, since they are highly contextual. For example, the users’ perceived affordances of an AR system for language learning while visiting a city overseas are very different to those perceived by a medical student who needs to develop a particular clinical skill.…”

Section: Affordancesmentioning

confidence: 99%

“…They also identified two modifiers that are important for AR: sensory vividness and physical context. 18 However, these categories have been developed specifically in the context of comparing VR and AR, so although we believe they are useful in identifying the affordances of AR when compared to VR (as some of the studies in the literature review have done) they cannot be adopted for other contexts uncritically.…”

Section: Affordancesmentioning

confidence: 99%

See 1 more Smart Citation

Current Perspectives on Augmented Reality in Medical Education: Applications, Affordances and Limitations

Parsons¹,

MacCallum²

2021

AMEP

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Section: Affordancesmentioning

confidence: 99%

Section: Affordancesmentioning

confidence: 99%

Current Perspectives on Augmented Reality in Medical Education: Applications, Affordances and Limitations

Parsons¹,

MacCallum²

2021

AMEP

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“…Three main fundamental collaboration-supporting mechanisms were identified in literature, which were found as useful particularly in VR supported collaboration in multi-faceted building design use cases: Adaptability, Agile trials, and High level of focus (Steffen et al, 2019). The interviewees were able to identify all these three fundamental mechanisms: Adaptability mechanism in 6 different use cases, Agile trials in 8 use cases and High level of focus in 6 different use cases.…”

Section: Fundamental Collaboration-supporting Mechanisms In Cvrmentioning

confidence: 93%

“…Thus, along with this development, it has been increasingly noticed that collaboration in and through VR means has produced significant benefits for companies and individuals in the enabling of better collaboration and knowledge transfer (e.g. (Slater and Sanchez-Vives, 2016;Steffen et al, 2019). Due to this novelty, there is yet a limited amount of research on the collaborative and social use of VR in the working context, in their use in the transfer of knowledge between different actors, and the use of CVR as a boundary object, especially in the AEC industry, as in our case.…”

Section: Virtual Reality and Cvrmentioning

confidence: 99%

Collaborative Virtual Reality as an Adaptable Boundary Object in the Design Phase of Facility Life Cycle

Lepola

Kärkkäinen

Jalo

et al. 2020

Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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Although the large majority of costs of buildings incur in the later operation and maintenance phase, major decisions affecting these costs are made in the early design and construction phases. Virtual Reality (VR) and Collaborative Virtual Reality (CVR) have been noticed to have significant potential in involving the expertise and needs of various stakeholders into the early design phases, increasing the quality of building designs and reducing related costs. Boundary Object Theory has been noticed useful in better understanding and improving the knowledge transfer of actors with different backgrounds and expertise. VR and CVR remain yet little studied as boundary objects. We will address this research gap in this study by aiming to understand how CVR can act as an adaptable boundary object in the building design phase of the facility life cycle. We have made use of a qualitative approach, consisting of a multiple case study approach and semi-structured interviews in Finnish AEC industry companies and organisations. We contribute to academic research by providing a deeper understanding of how CVR functions as a boundary object, which enhances the transfer of knowledge in new ways between various stakeholders in the building design phase.

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“…At the best of our knowledge, there is currently a lack of studies to theoretically explain why users maintain learning retention from technology feature perspectives. Users adopt or continuously use a tool since it affords special functionalities and action possibilities compared with others (Steffen, Gaskin, Meservy, Jenkins, & Wolman, 2019). Examining the affordances of AI-enabled automatic scoring applications contributes to the understanding of why learners continuously perform learning activity with AI-enabled automatic scoring applications.…”

Section: Related Background Ai Applications In Educationmentioning

confidence: 99%

The affordances of AI‐enabled automatic scoring applications on learners’ continuous learning intention: An empirical study in China

Fu¹,

Gu²,

Yang³

2020

Brit J Educational Tech

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Traditional educational giants and natural language processing companies have launched several artificial intelligence (AI)‐enabled digital learning applications to facilitate language learning. One typical application of AI in digital language education is the automatic scoring application that provides feedback on pronunciation repeat outcomes. This research is motivated by the usage of automatic scoring‐empowered digital learning tools by language learners, and set out to uncover the influencing mechanisms of AI‐enabled automatic scoring application affordances on learners’ continuous learning intention. Specifically, based on affordance theory, we found several automatic scoring application affordances through in‐depth interviews. Considering the current lack of investigations on the mechanisms underlying automatic scoring application and its implications for learners’ learning behaviors, we built a model to examine the role of automatic scoring application affordances on cognitive/emotional engagement and following continuous learning intention. We further examined the moderation role of in‐job learners and student learners on the above relationships. The model was tested using a survey of 260 Chinese foreign language learners who used AI‐empowered learning tools to facilitate their language learning practices. This study explores why learners continuously use AI‐enabled automatic scoring applications by identifying the affordances that differentiate it from traditional educational technologies. Practitioners could take the identified affordances into account when designing AI‐enabled language learning applications.

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Framework of Affordances for Virtual Reality and Augmented Reality

Cited by 187 publications

References 36 publications

Current Perspectives on Augmented Reality in Medical Education: Applications, Affordances and Limitations

Current Perspectives on Augmented Reality in Medical Education: Applications, Affordances and Limitations

Collaborative Virtual Reality as an Adaptable Boundary Object in the Design Phase of Facility Life Cycle

The affordances of AI‐enabled automatic scoring applications on learners’ continuous learning intention: An empirical study in China

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