Learning with Wearable Devices reveals Learners' Best Time to Learn

Yazawa, Satoshi; Yoshimoto, Hiromasa; Hiraki, Kei

doi:10.1145/3291078.3291097

Cited by 7 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The majority of current researches have concentrated on low-level recognition of basic movements, like standing, sitting, walking, running, and looking up (Almusawi et al, 2021; Zhong & Li, 2020; Zhong, 2021), or recognition of embodied learning behaviors, such as writing, discussing, raising hands, listening to teacher, and mind-wandering (Okur et al, 2017; Yazawa et al, 2018). Simple external behaviors play a supportive role in learning analytics, but there is growing interest in the relationship between learners' movements and higher psychological mechanisms such as cognition and emotion (Bowman et al, 2017; Camacho et al, 2020), representing their cognitive states and emotional experiences through their different movement behaviors.…”

Section: Discussionmentioning

confidence: 99%

Learning Analytics Based on Wearable Devices: A Systematic Literature Review From 2011 to 2021

Liu

Ren

Kong

et al. 2022

Journal of Educational Computing Research

View full text Add to dashboard Cite

Wearable devices are an emerging technological tool in the field of learning analytics. With the help of wearable technologies, an increasing number of scholars have a strong interest in studying the associations between student data and learning outcomes in different learning environments. This systematic review examines 120 articles published between 2011 and 2021, exploring current research on learning analytics based on wearable devices in detail from both descriptive and content analysis. The descriptive analysis reviewed the included literature in five dimensions: publication times of the reviewed literature, wearable devices and data types used in studies, stakeholders, objectives, and methods involved in the analysis procedure. The content analysis aims to examine the literature covered in terms of three categorical domains of educational objectives: cognitive, affective, and behavioral, to investigate the practical applications and potential issues of learning analytics based on wearable devices. After that, based on the overall research content of the reviewed literature, a framework for learning analytics based on wearable devices is present, and its application process is summarized and analyzed for the reference of related researchers. At last, we summarize the limitations of existing studies and present several recommendations to further promote research and development in this field.

show abstract