Designing Socially Acceptable Hand-to-Face Input

Lee, Do-Young; Lee, Youryang; Shin, Yonghwan; Oakley, Ian

doi:10.1145/3242587.3242642

Cited by 38 publications

(11 citation statements)

References 44 publications

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“…This could be because recruitment of participants depends on teachers and/or parents (legal guardian). MMD has not yet achieved a high level of social acceptance (Koelle et al ., 2018 ;Lee, Lee, Shin, & Oakley, 2018 ), which could be the reason why parents do not consent to their children participating in a study. Moreover, teachers might also think that data collection would hinder the normal learning process and be too intrusive.…”

Section: Student Feedbackmentioning

confidence: 99%

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Sharma¹,

Giannakos²

2020

Brit J Educational Tech

120

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Most research on learning technology uses clickstreams and questionnaires as their primary source of quantitative data. This study presents the outcomes of a systematic literature review of empirical evidence on the capabilities of multimodal data (MMD) for human learning. This paper provides an overview of what and how MMD have been used to inform learning and in what contexts. A search resulted in 42 papers that were included in the analysis. The results of the review depict the capabilities of MMD for learning and the ongoing advances and implications that emerge from the employment of MMD to capture and improve learning. In particular, we identified the six main objectives (ie, behavioral trajectories, learning outcome, learning‐task performance, teacher support, engagement and student feedback) that the MMLA research has been focusing on. We also summarize the implications derived from the reviewed articles and frame them within six thematic areas. Finally, this review stresses that future research should consider developing a framework that would enable MMD capacities to be aligned with the research and learning design (LD). These MMD capacities could also be utilized on furthering theory and practice. Our findings set a baseline to support the adoption and democratization of MMD within future learning technology research and development. What is already known about this topic Capturing and measuring learners’ engagement and behavior using MMD has been explored in recent years and exhibits great potential. There are documented challenges and opportunities associated with capturing, processing, analyzing and interpreting MMD to support human learning. MMD can provide insights into predicting learning engagement and performance as well as into supporting the process. What this paper adds Provides a systematic literature review (SLR) of empirical evidence on MMD for human learning. Summarizes the insights MMD can give us about the learning outcomes and process. Identifies challenges and opportunities of MMD to support human learning. Implications for practice and/or policy Learning analytics researchers will be able to use the SLR as a guide for future research. Learning analytics practitioners will be able to use the SLR as a summary of the current state of the field.

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Section: Student Feedbackmentioning

confidence: 99%

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Sharma¹,

Giannakos²

2020

Brit J Educational Tech

120

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“…In this equation, r is a referent in the set of all referents R, Pr is the set of proposals for referent r, and Pi is a subset of identical symbols from Pr. The range of the equation is: There is a reference to this in multiple gesture elicitations [107,74,20,34,39,66]. In addition, some works also refer to the taxonomy developed by Wobbrock et al [105,85,38].…”

Section: Agreement Rate Evaluationsmentioning

confidence: 99%

“…The AR (Agreement Rate) notation is used to differentiate from the first calculation. refer to this are also extensive [41,22,23,109,12,66]. In addition, some of these works also refer to the elicitation method used by Wobbrock et al [12].…”

Section: Agreement Rate Evaluationsmentioning

confidence: 99%

Evaluating User Preferences for Augmented Reality Interactions with the Internet of Things

Chopra

Maurer

2020

Proceedings of the International Conference on Advanced Visual Interfaces

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This thesis investigates how users want to control IoT devices in their homes with headset AR. Gestural and voice controls are both suitable methods of input for headset AR. However, there is a lack of end-user input in the design of such gestures and voice commands: especially in comparative terms. An elicitation study is performed with 16 participants to gather their preferred voice commands and gestures for a set of referents. The contribution is an analysis of 784 inputs (392 gestures and 392 voice), a resulting gesture set and command types, comparative method preferences, a novel method to analyze voice command input (called voice command pattern template), as well as design recommendations based on observations and interviews. These recommendations serve as guides for future designers and implementors of such voice commands and gestures.

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“…Concerning the flexibility of the ear, Kikuchi et al [14] proposes EarTouch, a new input method using several optical sensors attached to earphones to detect deformations of the ear. Lee et al [16] further explores the social acceptability of facial touches and found that the ear garners a high score in terms of the facial region and social acceptability. These previous studies show how interaction with the ears is possible from mainly a technical perspective.…”

Section: Interaction With the Earsmentioning

confidence: 99%

“…To address this issue, researchers have proposed instrumentalizing areas on the human body (e.g., the finger, limbs, or the face) for use as interactive spaces, as the skin and its surrounding space provide a larger input area and are very easily accessible. In addition, such interfaces allow users to communicate with wearable devices without the necessity of sight, in an "eyes-free" manner [14,16,18], making them especially usable in mobile scenarios. Among many areas of the body, the human ear is appealing for use as seen from commercially popular, head-worn devices, such as smart earpieces or augmented glasses.…”

Section: Introductionmentioning

confidence: 99%

Exploring User Defined Gestures for Ear-Based Interactions

Chen

Liao

Hsu

et al. 2020

Proc. ACM Hum.-Comput. Interact.

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The human ear is highly sensitive and accessible, making it especially suitable for being used as an interface for interacting with smart earpieces or augmented glasses. However, previous works on ear-based input mainly address gesture sensing technology and researcher-designed gestures. This paper aims to bring more understandings of gesture design. Thus, for a user elicitation study, we recruited 28 participants, each of whom designed gestures for 31 smart device-related tasks. This resulted in a total of 868 gestures generated. Upon the basis of these gestures, we compiled a taxonomy and concluded the considerations underlying the participants' designs that also offer insights into their design rationales and preferences. Thereafter, based on these study results, we propose a set of user-defined gestures and share interesting findings. We hope this work can shed some light on not only sensing technologies of ear-based input, but also the interface design of future wearable interfaces. CCS Concepts: • Human-centered computing → Gestural input; User studies.

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Designing Socially Acceptable Hand-to-Face Input

Cited by 38 publications

References 44 publications

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Evaluating User Preferences for Augmented Reality Interactions with the Internet of Things

Exploring User Defined Gestures for Ear-Based Interactions

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