AdapTutAR: An Adaptive Tutoring System for Machine Tasks in Augmented Reality

Huang, Gaoping; Qian, Xun; Wang, Tianyi; Patel, Fagun; Sreeram, Maitreya; Cao, Yichao; Ramani, Karthik; Quinn, Alexander J.

doi:10.1145/3411764.3445283

Cited by 39 publications

(13 citation statements)

References 59 publications

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“…For instance, Tahara et al [48] employ a scene graph to define the spatial relationships between virtual and physical objects, automatically adjusting the virtual content when the user moves to other environments. AdapTutAR [21], an AR task tutoring system, adjusts the teaching content adaptively based on the user's characteristics. SemanticAdapt [10] uses computer vision techniques to detect the category of real objects and associates them with AR content.…”

Section: Related Workmentioning

confidence: 99%

A meta-framework for multi-label active learning based on deep reinforcement learning

2023

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Section: Related Workmentioning

confidence: 99%

A meta-framework for multi-label active learning based on deep reinforcement learning

2023

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“…Together they affect the engagement and consequently the learning outcomes of each individual learner. While there are studies such as [36,93] that provide adaptive guidance for performing a physical task, generally there is a lack of research on providing adaptive guidance based on the amount or type of instructions. Moreover, we are unaware of any work that focuses on the "keyword method" as the type of guidance used to support language learning in AR environments.…”

Section: Language Learning In Armentioning

confidence: 99%

Arigatō: Effects of Adaptive Guidance on Engagement and Performance in Augmented Reality Learning Environments

Weerasinghe

Quigley

Pucihar

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Arigat ō prototype and its experiential learning stages for two different topics: Christmas and birthday celebration. (a) Reflective observation (RO), (b) Abstract conceptualisation (AC), and (c) Active experimentation (AE), stages for fixed-associations condition on the Christmas celebration topic. (d) Concrete experience (CE) for both topics. (e) Active experimentation (AE), (f) Abstract conceptualisation (AC), and (g) Reflective observation (RO) stages for adaptive-associations condition on a birthday celebration topic. To avoid excessive clutter of the AR scene the black background was selected.

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“…In one study [31], all experiments were performed in the two first authors' homes, and by the authors only, due to logistic constraints caused by the pandemic. For remote studies during Q3, researchers most commonly justified their methods by referring to current restrictions in place; meaning they had limited accessibility to shared facilities, equipment and users [37,42], and had to adhere to COVID safety measures [8,26,37].…”

Section: Covid-19 Considerations: Reported Vs Not Statedmentioning

confidence: 99%

“…Due to limitations surrounding COVID-19, some researchers opted to use VR technologies to simulate AR experiences [26], with Schott et al [52] unable to test their AR set up with multiple users.…”

Section: Covid-19 Considerations: Reported Vs Not Statedmentioning

confidence: 99%

Socially Distanced: Have user evaluation methods for Immersive Technologies changed during the COVID-19 pandemic?

Spittle

Frutos-Pascual

et al. 2021

2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)

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Since the emergence of COVID-19 in late 2019, there has been a significant disturbance in human-to-human interaction that has changed the way we conduct user studies in the field of Human-Computer Interaction (HCI), especially for extended (augmented, mixed, and virtual) reality (XR). To uncover how XR research has adapted throughout the pandemic, this paper presents a review of user study methodology adaptations from a corpus of 951 papers. This corpus of papers covers CORE 2021 A* published conference submissions, from Q2 2020 through to Q1 2021 (IEEE ISMAR, ACM CHI, IEEE VR). The review highlights how methodologies were changed and reported; sparking discussions surrounding how methods should be conveyed and to what extent research should be contextualised, by drawing on external topical factors such as COVID-19, to maximise usefulness and perspective for future studies. We provide a set of initial guidelines based on our findings, posing key considerations for researchers when reporting on user studies during uncertain and unprecedented times.

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AdapTutAR: An Adaptive Tutoring System for Machine Tasks in Augmented Reality

Cited by 39 publications

References 59 publications

A meta-framework for multi-label active learning based on deep reinforcement learning

A meta-framework for multi-label active learning based on deep reinforcement learning

Arigatō: Effects of Adaptive Guidance on Engagement and Performance in Augmented Reality Learning Environments

Socially Distanced: Have user evaluation methods for Immersive Technologies changed during the COVID-19 pandemic?

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