Enabling active learning experience in XR environments – identifying the design elements for XR learning objects

Abstract: This research focuses on identifying the design elements for XR learning objects. In this research the data gathered is focusing on the Finnish secondary level education pilot projects which use XR-environments. In this concept paper we present the identified phases for data collection. The identified phases are teacher’s design documents, the planning, implementing phases and reflection phase. Data is collected from teachers that are designing the learning object for XR environment.Extended Reality (XR) is an umbrella term for virtual, mixed and augmented realities. Commonly they’re known as Virtual Reality (VR), Mixed Reality (MR) and Augmented Reality (AR). VR is usually associated into Head Mounted Displays (HMD) and they’re referred as VR-glasses in common language. VR-glasses are basically creating stereoscopic picture and it creates the user feeling like they’re immersed in the 3-dimensional space. (Milgram & Kishino, 1994)XR-environments provides many possibilities to support learning. When XR is used effectively in learning, it increases the participant’s interest in and focus on the learning task. XR makes it possible to look at things that would not otherwise be so well illustrated in the real world. Augmented reality increases students motivation and helps them explore the existing environment (Sotiriou & Bogner 2008). Several studies show that the use of AR in education leads to enhanced learning outcomes (Akçayır & Akçayır 2016).Data is collected nowadays everywhere but there’s a big gap in utilizing the data for the purposes to develop learning and pedagogy. Most of the data applied is for business and management purposes. The term Educational data mining has been firstly mentioned in half of the 2005 and since that the applications and research of this field has been growing. One of the substudy fields of EDM is Learning Analytics which is defined as measuring, collecting, analysing and reporting of data from acquired from learning environments (Larusson & White, 2014; Siemens & Baker, 2013). Learning analytics interprets the collected data from various sources. Data is collected from various learning environments which leave “digital footprints” from student interactions. The data is analysed to make predictions, to visualize learning progress and to make interventions in learning processes. Learning analytics field doesn’t limit only to algorithm based interpretations of learning but it could utilize different techniques to analyze the data properly. (Johnson et al, 2011).

Keywords: virtual reality, pedagogy, learning analytics, augmented reality, learning

DOI: 10.54941/ahfe1003904

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