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

Abstract

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.

Computational Learning Analytics to Estimate Location-Based Self-Regulation Process of Real-World Experiences

“…Traditionally, in various educational settings (e.g., mathematical learning [28], language acquisition [29], engineering education [30], learning from others [31], inquirybased classroom learning [32], mastery-based online learning [33]), a strategy is considered as a way to solve a problem or to achieve a particular intellectual achievement (e.g., way of inference, problem solving, surveying, collaboration, self-regulation) [4]. Recent studies on individual-level selfregulation of learning (SRL) and group-level self-regulation of learning (i.e., socially shared regulation of learning; SSRL) have maintained a focus on learning in indoor settings (e.g., classrooms, online, museums) [15], [16], [34], rather than realworld learning.…”

Contextualizing privacy with wearable data in higher education

“…Research has shown wearables used in learning environments can increase enjoyment, provide ubiquitous communication and offer learning mobility (Al-Emran et al, 2020). Wearable data expand the type of data available to learning analytics such as adding motor behaviour, learning environment information and physiological information (see Table 1; Liu et al, 2022).…”

“…Learning analytics is the use of data to conduct a statistical analysis to provide explanatory power and predictive models to educational phenomenon (Arroway et al, 2015) to gain insights into how students learn (Joksimović et al, 2019). In a literature review from 2011 to 2021, Liu et al (2022) categorized the use of research on learning analytics with wearable technology into four areas: data collection, data analysis and modelling, monitoring and visualization, and intervention and regulation. In this paper, we highlight the data collection, data analysis and communication of wearable data alongside privacy implications for each.…”

Toward Mental Effort Measurement Using Electrodermal Activity Features

“…These have transformed from relatively invasive devices used in healthcare or research settings to now being an integral part of popular consumer devices, such as smartwatches. In the past few years, researchers have begun synthesizing the literature around the use of wearable sensors in educational settings, with a focus on the impacts on learning [ 4 ] and their use in learning analytics [ 5 ].…”