Use of wearable devices in the teaching-learning process: a systematic review of the literature

被引:3
作者
Glasserman-Morales, Leonardo David [1 ,2 ]
Carlos-Arroyo, Martina [2 ]
Ruiz-Ramirez, Jessica Alejandra [1 ]
Alcantar-Nieblas, Carolina [2 ]
机构
[1] Tecnol Monterrey, Sch Humanities & Educ, Monterrey, NL, Mexico
[2] Tecnol Monterrey, Inst Future Educ, Monterrey, NL, Mexico
关键词
wearable devices; multi-modal learning analytics; teaching-learning processes; biometric indicators; educational innovation; HIGHER-EDUCATION; ANALYTICS; TECHNOLOGY; KNOWLEDGE; BENEFITS; SIGNALS;
D O I
10.3389/feduc.2023.1220688
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Multimodal learning analytics (MMLA) has emerged as an encompassing approach to data collection, facilitating the analysis of student interactions across a variety of resources. MMLA capitalizes on data gleaned from diverse interactions, utilizing wearable devices to track physiological responses. This yields deeper insights into factors such as cognitive load, stress levels, interest, and other stimuli pivotal to the learning process. Nonetheless, it is crucial to acknowledge the theoretical and practical challenges underpinning the integration of wearable devices into learning experiences, both in academic settings and in everyday life activities. A systematic review of the literature (SLR) was conducted to identify the characteristics of studies that incorporate wearable devices into teaching-learning process analyses. The outcomes enabled us to discern key attributes such as participant descriptions, the activities implemented for data collection, and a broad spectrum of biometric indicators, with electrodermal activity (EDA) and heart rate (HR) among the most commonly employed methodologies in data analysis. Future endeavors should be centered on the formation of interdisciplinary teams. The objective is to devise novel methodologies for multimodal data collection and analysis that can discern performance variables, thereby enhancing learning in a manner conducive to more fluid, reflective educational experiences for all participants in the teaching-learning process.
引用
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页数:10
相关论文
共 62 条
[1]   Advanced Learner Assistance System's (ALAS) Recent Results [J].
Aguilar-Herrera, Aime Judith ;
Delgado-Jimenez, Esther Aimee ;
Candela-Leal, Milton Osiel ;
Olivas-Martinez, Gustavo ;
Alvarez-Espinosa, Gabriela Jazmin ;
Ramirez-Moreno, Mauricio Adolfo ;
Lozoya-Santos, Jorge de Jesus ;
Ramirez-Mendoza, Ricardo Ambrocio .
2021 MACHINE LEARNING-DRIVEN DIGITAL TECHNOLOGIES FOR EDUCATIONAL INNOVATION WORKSHOP, 2021,
[2]   "That Student Should be a Lion Tamer!" StressViz: Designing a Stress Analytics Dashboard for Teachers [J].
Alfredo, Riordan Dervin ;
Nie, Lanbing ;
Kennedy, Paul ;
Power, Tamara ;
Hayes, Carolyn ;
Chen, Hui ;
McGregor, Carolyn ;
Swiecki, Zachari ;
Gasevic, Dragan ;
Martinez-Maldonado, Roberto .
THIRTEENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, LAK2023, 2022, :57-67
[3]  
Avella JT, 2016, ONLINE LEARN, V20, P13, DOI 10.24059/olj.v20i2.790
[4]  
Borrego A., 2019, 2019 International Conference on Virtual Rehabilitation (ICVR), P1, DOI [DOI 10.1109/ICVR46560.2019.8994546, 10.1109/ICVR46560.2019, DOI 10.1109/ICVR46560.2019]
[5]  
Boucsein W, 2012, ELECTRODERMAL ACTIVITY, SECOND EDITION, P1, DOI 10.1007/978-1-4614-1126-0
[6]   Multiclass emotion prediction using heart rate and virtual reality stimuli [J].
Bulagang, Aaron Frederick ;
Mountstephens, James ;
Teo, Jason .
JOURNAL OF BIG DATA, 2021, 8 (01)
[7]   Wearables for Engagement Detection in Learning Environments: A Review [J].
Bustos-Lopez, Maritza ;
Cruz-Ramirez, Nicandro ;
Guerra-Hernandez, Alejandro ;
Nely Sanchez-Morales, Laura ;
Aracely Cruz-Ramos, Nancy ;
Alor-Hernandez, Giner .
BIOSENSORS-BASEL, 2022, 12 (07)
[8]  
Chandra Varun, 2021, Advances in Computing and Data Sciences: 5th International Conference, ICACDS 2021. Communications in Computer and Information Science (1441), P218, DOI 10.1007/978-3-030-88244-0_21
[9]   Improving Heart Rate Estimation on Consumer Grade Wrist-Worn Device Using Post-Calibration Approach [J].
Choksatchawathi, Tanut ;
Ponglertnapakorn, Puntawat ;
Ditthapron, Apiwat ;
Leelaarporn, Pitshaporn ;
Wisutthisen, Thayakorn ;
Piriyajitakonkij, Maytus ;
Wilaiprasitporn, Theerawit .
IEEE SENSORS JOURNAL, 2020, 20 (13) :7433-7446
[10]   Dataset of acceleration signals recorded while performing activities of daily living [J].
Climent-Perez, Pau ;
Munoz-Anton, Angela M. ;
Poli, Angelica ;
Spinsante, Susanna ;
Florez-Revuelta, Francisco .
DATA IN BRIEF, 2022, 41