Moving Bodies to Moving Minds: A Study of the Use of Motion-Based Games in Special Education

Kosmas, Panagiotis; Ioannou, Andri; Retalis, Symeon

doi:10.1007/s11528-018-0294-5

Cited by 58 publications

(45 citation statements)

References 30 publications

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“…A post hoc mapping between the modalities used in the studies and the sample size reveals that the majority of the studies with a sample size larger than 40 used data streams such as audio, video, logs and surveys (for the details of the mapping for the studies with more than 40 participants, see Appendix D ). On the contrary, most of the other studies had more sophisticated data collection equipment, such as eye-tracking , EEG (Giannakos et al ., 2019 ;Sharma, Papamitsiou, et al ., 2019 ), EDA sensors (Worsley & Blikstein, 2015 ) and Kinect (Kosmas, Ioannou, & Retalis, 2018 ). This ease of collection of data possibly explains the higher number of participants in the first set of studies.…”

Section: Data Collection Sample Size and Methodologymentioning

confidence: 99%

“…Behavioral trajectories (process) Several researchers have pointed out how MMLA is capable of explaining learning processes or students' trajectories. Specifically, Kosmas et al . ( 2018 ) showed in a Kinect-based game that the play-time and range of motion improve students' short-term memory; Junokas et al .…”

Section: For Learning Behavior and Performancementioning

confidence: 99%

“…Therefore, the studies report that MMD can be successfully used to explain students' trajectories while the students engage in the learning task. From the aforementioned studies, we can see that interaction logs, gestures and posture were related to memory (Junokas et al ., 2018 ;Kosmas et al ., 2018 ), conceptual understanding (Andrade et al ., 2017 ), the artifact quality (Spikol et al ., 2018 ) and cognitive workload and motivation (Mock et al ., 2016 ).…”

Section: For Learning Behavior and Performancementioning

confidence: 99%

See 2 more Smart Citations

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: Data Collection Sample Size and Methodologymentioning

confidence: 99%

Section: For Learning Behavior and Performancementioning

confidence: 99%

Section: For Learning Behavior and Performancementioning

confidence: 99%

See 1 more Smart Citation

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

Sharma¹,

Giannakos²

2020

Brit J Educational Tech

120

View full text Add to dashboard Cite

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“…Body movement and gestures constitute an aspect of our everyday life which is natural for everybody: therefore, the challenge of turning instinctive modalities to communicate messages with non-living objects provides a fascinating user experience [23]. Body movement in the form of walking in a room can act as an interaction method via computer-vision based noninstrumented human detection, facilitating both implicit and explicit interaction not only in CH installations [24] but also within educational contexts [25] [26]. Another common interaction technique is mid-air hand gesturing: users are able to simply point in order to retrieve additional information about exhibited artefacts [27], browse a multimedia collection [28] reveal additional information by lighting exhibits [29].…”

Section: Applications and Interaction In Cultural Heritage Environmentsmentioning

confidence: 99%

Enhancing accessibility in cultural heritage environments: considerations for social computing

Kosmas

Galanakis

Constantinou

et al. 2019

Univ Access Inf Soc

Self Cite

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Current technological advancements offer many ways of enhancing disabled peoples' access to Cultural Heritage environments. A new generation of Social Computing technologies and systems is changing the way in which we access Cultural Heritage, facilitating the inclusion of socially isolated groups of people. Under this perspective, this paper aims to explore the potential impact of Social Computing systems to enhance people' access to Cultural Heritage, particularly focusing on deaf and disabled users. By reviewing the current literature on Social Computing and Cultural Heritage, the paper first summarizes the related applications and appropriate key technologies; second, it provides examples of innovative approaches to the enhancement of user-engagement and interaction through social computing. Moreover, the paper highlights arising issues of privacy, as well as ethical considerations, and presents design principles for ensuring privacy. The study concludes by discussing challenges for inclusive Social Computing applications in the context of Cultural Heritage and pointing out areas where future research is needed. AbstractCurrent technological advancements offer many ways of enhancing disabled peoples' access to Cultural Heritage environments. A new generation of Social Computing technologies and systems is changing the way in which we access Cultural Heritage, facilitating the inclusion of socially isolated groups of people. Under this perspective, this paper aims to explore the potential impact of Social Computing systems to enhance people' access to Cultural Heritage, particularly focusing on deaf and disabled users. By reviewing the current literature on Social Computing and Cultural Heritage, the paper first summarizes the related applications and appropriate key technologies; second, it provides examples of innovative approaches to the enhancement of user-engagement and interaction through social computing. Moreover, the paper highlights arising issues of privacy, as well as ethical considerations, and presents design principles for ensuring privacy. The study concludes by discussing challenges for inclusive Social Computing applications in the context of Cultural Heritage and pointing out areas where future research is needed.[1]. Based on the literature, SC can be interactive and collaborative as it facilitates communication between people. SC environments allow people to interact in many different ways. Examples of SC technologies include social media sites and networks, e-mail, discussion forums, blogs, wikis, instant messaging, games, and open-source development.More recently, the rise of SC tools has influenced information systems and applications and strongly received researchers' attention with a particular interest to its social implications for helping people access Cultural Heritage environments [2]. Indeed, social technologies, and especially social media applications, affect both access to and the character of the cultural activity. Cultural Heritage (CH) constitutes an interdisciplinary area...

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“…A number of studies in SC and Educational Technology have been conducted to support both children and adults with different health problems and impairments. Many technologies, under the field of game-based learning, such as motion-based interactive games, appear to enhance the academic, cognitive and motor skills of children with disabilities [24,25]. The study of Constantinou et al (2016), examined the personal tour of cultural heritage for deaf museum visitors through a mobile application.…”

Section: Design and Inclusion Of People With Disabilitiesmentioning

confidence: 99%

Towards the Use of Social Computing for Social Inclusion: An Overview of the Literature

Constantinou

Kosmas

Parmaxi

et al. 2018

Learning and Collaboration Technologies. Design, Development and Technological Innovation

Self Cite

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Social computing constitutes a new computing paradigm in an interdisciplinary research community which will strongly influence informatics systems and applications in the years to come. Indeed, from a social perspective, considerable interest in examining the influences of Social Computing (SC) for Social Inclusion (SI) arises from researchers and designers. This paper addresses the existing literature on the use of SC for SI. Previous studies have been reviewed to report affordances of SC design in five different areas: (a) for the elderly, (b) for people with disabilities, (c) for gender studies, (d) for societal change, and (e) for the preservation of cultural heritage. Results from the literature overview provide some examples of applications of new technologies that were designed to reduce social exclusion. Our review further shows that the use of different tools used by different groups of people has a positive impact for those people who are either socially included or excluded from their use. However, further research is needed in these five directions, which are analysed in this document and direct research into the gaps identified.

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Moving Bodies to Moving Minds: A Study of the Use of Motion-Based Games in Special Education

Cited by 58 publications

References 30 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?

Enhancing accessibility in cultural heritage environments: considerations for social computing

Towards the Use of Social Computing for Social Inclusion: An Overview of the Literature

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