From Sensor Data to Educational Insights

Dear Colleagues,

Technology is gradually being incorporated as an integral part of learning at all educational levels. This technology includes the now pervasive presence of virtual learning environments (VLEs), but also the inclusion of devices that are used/worn by learners or that are present in the classroom. This new educational ecosystem has greatly facilitated data capture about learners, and 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. The inferences about learning that can be made by solely analyzing trace data from VLEs are rather limited. Therefore, the research communities have started to move beyond the data obtained from these VLEs 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), but the use of these data sources is also common in broader research areas, such as affective computing or human-computer interaction (HCI). The promise is to potentially augment and improve the extent and quality of the analysis that can be performed with these new data sources. The challenge is how to embed sensors and resulting data representations in authentic educational settings in pedagogically meaningful and ethical ways.

In this Special Issue, we welcome publications that include approaches to convert data captured using sensors (e.g., cameras, smartphones, microphones or temperature sensors), wearables (e.g., smart wristbands, watches, or glasses) or other IoT devices (e.g., interactive whiteboards, eBooks or tablets) into meaningful educational insights. The submitted articles need to appropriately explain how the inclusiveness of data from such devices can augment the analyses performed to improve teaching, learning or the educational context where it occurs (e.g., in classrooms, VLEs or other educational spaces).

This Special Issue focuses on all kinds of empirical case studies that fulfil the aforementioned criteria, but also experimental architectures or positioning/survey papers. The topics of interest include but are not limited to:

• Empirical case studies that include data from sensors and IoT devices to make an impact in teaching and learning practices;
• Learner modeling and intelligent tutoring using multimodal data sources;
• Critical views or theoretical perspectives regarding how to transform data from these sensors and IoT devices into educational insights;
• Systematic literature reviews or surveys about the role of data from sensors and IoT devices in research areas such as LA, EDM, AIED, affective computing or HCI to improve education;
• Architectures or frameworks to manage the orchestration of these sensors and IoT devices to improve education;
• Privacy, security, and ethical concerns about the use of these sensor data in educational settings.

Dr. José A. Ruipérez-Valiente
Dr. Roberto Martinez-Maldonado
Dr. Daniele Di Mitri
Dr. Jan Schneider
Guest Editors

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