Smartwatch-Centered Design and Development in Mobile Computing Classes

Esakia, Andrey; Kotut, Lindah

doi:10.1109/fie44824.2020.9274280

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…The analysis and training of physiological data, like HR, ECG, and EEG, allows for real-time monitoring of students’ physiological status (Antoniou et al, 2020; Ahonen et al, 2018; Aritzeta et al, 2017; Lew & Tang, 2017; Lin & Kao, 2018). On the other hand, the exploration and study of movement data, such as gyroscope and tri-axial accelerometer data, can help physical education teachers to accurately grasp students’ movement levels and provide appropriate guidance (Liang et al, 2021; Li et al, 2019; Lee et al, 2016; Frömel et al, 2021; Esakia & Kotut, 2020). Figure 4 represents the classification of the reviewing articles depending on the types of wearable data.…”

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

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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.

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