Analítica multimodal del aprendizaje en Chile: Una revisión sistemática

Monsalves, Diego; Reyes, Héctor; Riquelme, Fabián

doi:10.18259/acs.2023008

Cited by 2 publications

(1 citation statement)

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“…Crucial contextual student data, vital for understanding behaviors and influential factors in their learning process, are often omitted (Eradze and Laanpere, 2017). To address these limitations, multimodal learning analytics (MMLA) has been introduced as a comprehensive approach to data collection (Giannakos et al, 2022;Monsalves et al, 2023). MMLA is characterized by the study of data from diverse sources stemming from student interactions in a range of environments beyond just the digital; this includes physical, physiological, psychometric, and environmental spaces (Worsley et al, 2016;Mu et al, 2020;Giannakos et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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

Glasserman-Morales,

Carlos-Arroyo,

Ruiz-Ramirez

et al. 2023

Front. Educ.

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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.

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