Giacomo Nalli, Georgios Dafoulas, Ariadni Tsiakara, Bahareh Langari, Kajal Mistry, Farzad Tahmasebi Aria
pp. 39 – 52, download
(https://doi.org/10.55612/s-5002-058-001)
Abstract
The use of hybrid educational environments, especially after COVID-19 pandemic, has intensified leading to new pedagogic challenges as the integration of biometric sensors into learning processes. Instructors must adapt their methods so that teaching and its quality are not negatively affected. The aim of this study is to enhance the understanding of students’ learning experiences by analysing biometric data during different learning activities. This paper focuses on the use of an Internet of Things (IoT) device, to collect Galvanic Skin Response (GSR) and Heart rate (HR) levels, in addition to biometric data. The quantitative analysis of the collected data shows a correlation between the data extracted that allow us to detect changes on students’ emotions. Subsequently the data analysis is used by instructors to provide formative feedback to enhance student learning, benefiting learners in terms of self-directed learning and motivation which can help them to improve their performance. The paper illustrates a case study of a hybrid learning university learning activity adopted in undergraduate programmes.
Keywords: Internet of Things, hybrid learning, emotion detection
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