ARMath: Augmenting Everyday Life with Math Learning

Kang, Seokbin; Shokeen, Ekta; Byrne, Virginia L.; Norooz, Leyla; Bonsignore, Elizabeth; Williams-Pierce, Caro; Froehlich, Jon E.

doi:10.1145/3313831.3376252

Cited by 42 publications

(24 citation statements)

References 81 publications

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“…Dove et al [209] surveyed to understand how design innovation is practiced in the ML domain in terms of user experience. [63], [70], [165], [174], [211] [124], [125], [128], [175], [217] [69], [112], [113], [118], [130], [131], [133], [135], [137], [140], [144], [152], [153], [155] [49], [57], [67], [98], [110], [114], [180], [192], [193], [203], [214], [215] [48], [59], [62], [75], [79], [111], [120], [156], [159], [185], [195], [197], [207], [218] [65], [83],…”

Section: Survey Resultsmentioning

confidence: 99%

“…Jensen et al [ 124 ] present a system to generate feedback for teachers in classrooms automatically. ARMath [ 125 ] is a Deep Learning powered tool for children to discover mathematical concepts with real-life objects with augmented reality. MaraVis [ 126 ] is a Deep Learning powered tool for real-time urban marathon visualization and coordinated intervention.…”

Section: Classifying Hcml Researchmentioning

confidence: 99%

“…Considering the works that focused on the user side, some researchers catered to general end-users or consumers [83,101,200,210], while others on specific end-users. Examples for these include people who need assistance [2,80,[86][87][88][89]96,117,147], medical professionals [57,67,110,192,193], international travelers [50], Amazon Mechanical Turk [60,99], drivers [161,162], musicians [102], teachers [124], students [128], children [72,125], UX designers [65,115,206,209], UI designers [103,111,173], data analysts [97], video creators [84], and game designers [70,165,174,211]. Apart from focusing on a specific user group, some have tried to understand multiple user-perspectives from ML engineers to the end-user [48].…”

Section: The 'Human' In Hcmlmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

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After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

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Section: Survey Resultsmentioning

confidence: 99%

Section: Classifying Hcml Researchmentioning

confidence: 99%

Section: The 'Human' In Hcmlmentioning

confidence: 99%

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A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

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“…Using this technique, GeoGebra AR [27] allows students to mathematically construct 3D models virtually placing them in the real world and virtually testing the ac-curacy of the created models, allowing to improve them. ARMaths [14] allows us to work with concepts such as addition, subtraction, multiplication, etc. The manipulation of tangible and virtual objects in a mixed reality and in a real environment is compared in the work.…”

Section: State Of the Artmentioning

confidence: 99%

Multimedia augmented reality game for learning math

Rebollo

Remolar

Rossano

et al. 2021

Multimed Tools Appl

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The traditional method for learning the multiplication tables is a repetitive and boring task. Teachers try to find new methods to motivate children in this tedious duty, and one of the lines to consider is to integrate en- tertainment into educational processes. This work presents a new multimedia interaction approach in order to allow children to practice these math opera- tions and have fun. The learning process has been gamified by means of two mini-games designed for mobile platforms, based on meromictic or repetitive learning. The genre of these mini-games have been selected according to chil- dren preferences: one turn-based fighting and other throwing-objects game. A series of proposed multiplications have to be solved during the play to per- form the player actions. Moreover, in order to support learning engagement, both have been visualized through Augmented Reality, combining real and virtual reality. This paper discusses the good results of mixing entertainment with some learning tasks, due to the engagement of the children to the mobile based games. A pilot study has been performed in order to evaluate the learn- ing effectiveness and usability of the proposal. Results support that playing the video games makes this tedious multiplication practice more enjoyable and attractive for children so they improve their math skills.

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“…In mathematics in particular, well-designed games can provoke players to voluntarily mathematize the digital context in a process called theorycrafting (e.g., Steinkuehler and Williams, 2009;Devlin, 2011). Consequently, numerous educational designersacademic (e.g., Barab et al, 2010;Gresalfi and Barnes, 2016;Zandieh et al, 2018;Kang et al, 2020) as well as commercial (e.g., Ritzhaupt et al, 2009)-have therefore developed learning games. However, the quality of these games is inconsistent: too many learning games mix the "entertainment value of a bad lecture with the educational value of a bad game" (Squire and Jenkins, 2003, p. 8).…”

Section: Motivating Games For Mathematics Learningmentioning

confidence: 99%

The Future of Embodied Design for Mathematics Teaching and Learning

et al. 2020

Self Cite

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A rising epistemological paradigm in the cognitive sciences-embodied cognition-has been stimulating innovative approaches, among educational researchers, to the design and analysis of STEM teaching and learning. The paradigm promotes theorizations of cognitive activity as grounded, or even constituted, in goal-oriented multimodal sensorimotor phenomenology. Conceptual learning, per these theories, could emanate from, or be triggered by, experiences of enacting or witnessing particular movement forms, even before these movements are explicitly signified as illustrating target content. Putting these theories to practice, new types of learning environments are being explored that utilize interactive technologies to initially foster student enactment of conceptually oriented movement forms and only then formalize these gestures and actions in disciplinary formats and language. In turn, new research instruments, such as multimodal learning analytics, now enable researchers to aggregate, integrate, model, and represent students' physical movements, eye-gaze paths, and verbal-gestural utterance so as to track and evaluate emerging conceptual capacity. We-a cohort of cognitive scientists and design-based researchers of embodied mathematics-survey a set of empirically validated frameworks and principles for enhancing mathematics teaching and learning as dialogic multimodal activity, and we synthetize a set of principles for educational practice.

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ARMath: Augmenting Everyday Life with Math Learning

Cited by 42 publications

References 81 publications

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Multimedia augmented reality game for learning math

The Future of Embodied Design for Mathematics Teaching and Learning

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