Scaffolding augmented reality inquiry learning: the design and investigation of the TraceReaders location-based, augmented reality platform
Journal
Interactive Learning Environments
Date Issued
February 17, 2019
Author(s)
DOI
10.1080/10494820.2018.1458039
Abstract
While learning can happen anywhere and everywhere, most educational practices in K-12 are confined within the walls of a classroom and the school; such practices narrowly define learning and exclude the opportunities that an expanded and digitally mediated definition of learning can offer. Augmented reality (AR) technologies offer exciting new opportunities for supporting ubiquitous learning, by superimposing layers of digital information on the real world. The digital augmentation can provide enriched learning experiences, through situating the learning content in authentic contexts and fostering inquiry-based learning. Nonetheless, learning can often be sidestepped as the use of AR technologies becomes a mere fun activity, akin to a treasure hunt. Such challenges indicate the need to provide scaffolded AR environments to support deep learning. These ideas are reflected in the design of the TraceReaders, a platform for enabling location-based mobile learning using augmented reality (AR) technologies. TraceReaders supports the authoring of inquiry-based AR apps, to engage students in evidence-driven reflective inquiry in situ. This paper first describes the theoretical commitments which guided the development of the TraceReaders platform, followed by a description of its design rationale. Two case studies of informal inquiry learning using TraceReaders are then presented: the first one reports on the use of the “Young Archaeologists” TraceReaders app to support primary school students’ historical reasoning, while the second one reports on the “Mystery at the Lake” app to support high school students’ environmental science inquiry. These cases offer the opportunity to discuss the affordances and challenges in using such a scaffolded tool to support location-based AR learning in situ. The discussion concludes with lessons learned from empirical studies about the design and effectiveness of tools like the TraceReaders platform and future steps.