From sensor data to educational insights.

Autor/inn/en	Ruipérez-Valiente, José A.; Martínez-Maldonado, Roberto; Di Mitri, Daniele; Schneider, Jan
Titel	From sensor data to educational insights.
Quelle	In: Sensors, 22 (2022) 21, S. 1-6 PDF als Volltext Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	1424-8220
DOI	10.3390/s22218556
Schlagwörter	Künstliche Intelligenz; Lernprozess; Datenanalyse; Datenerfassung; Mensch-Maschine-Kommunikation; Messverfahren; E-Learning; Übersicht; Logdatei; Tool
Abstract	Technology is gradually becoming an integral part of learning at all levels of educational. This includes the now pervasive presence of virtual learning environments (VLEs) and the inclusion of interactive devices used or worn by learners or that are present in the physical classroom environment. These new technology-rich educational ecosystems have greatly facilitated data capture about learners. Thus, several research areas, such as learning analytics (LA), educational data mining (EDM), and artificial intelligence in education (AIED), have grown exponentially during the last decade, with multiple venues supporting this research [1]. However, the inferences about learning that can be made by solely analyzing trace data from VLEs are limited, since logged data do not commonly provide a complete view of the learning experience [2]. Therefore, research communities are moving beyond the data obtained from VLEs and other online tools by incorporating data from external sources such as sensors, pervasive devices, and computer vision systems. Within the context of education, this subfield is often denominated as multimodal learning analytics (MMLA) [3]; nevertheless, the use of these data sources is also common in broader research areas, such as affective computing (e.g., [4]) and human-computer interaction (HCI) (e.g., [5]). The promise is to augment and improve the extent and quality of the analysis that can be performed with these new data sources [6]. Moreover, many new sensor-based tools, such as sensor-based games [7] or realistic laboratories [8,9], are being built to support the educational process. The challenge is embedding sensors and resulting data representations in authentic educational settings in pedagogically meaningful and ethical ways [10]. This Special Issue (SI) invited publications that include approaches to converting data captured using sensors (e.g., cameras, smartphones, microphones, or temperature sensors), wearables (e.g., smart wristbands, watches, or glasses), or other Internet of Things (IoT) devices (e.g., interactive whiteboards, eBooks, or tablets) into meaningful educational insights. Moreover, it invited papers on tools, architectures, or frameworks to manage the orchestration of these sensors and IoT devices to improve education. The submitted articles had to appropriately explain how the inclusiveness of sensor devices can augment the analyses performed to improve teaching, learning, or the educational context in which the sensing it occurs (e.g., in classrooms, VLEs, or other educational spaces). This SI has focused on empirical case studies that fulfill the aforementioned criteria and experimental architectures, methodologies, frameworks, or survey papers. (DIPF/Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2023/1