They believe students can fly: A scoping review on the utilization of drones in educational settings

In the past decade, drones have become another cutting-edge technology for educators, especially those in STEM-related domains. Accordingly, there is a significant need to thoroughly examine how drones are integrated into current pedagogical practices. This study scopes the domain of drone-based learning based on a collection of forty-eight articles identified via systematic searches across the Web of Science (WoS) databases. The analytical framework for coding is underpinned by the Substitution-Augmentation-Modification-Redefinition (SAMR) model. The review explored trends, domains and pedagogical activities, research approaches, learners and learning objectives, variables and aspects of interest, and most importantly, the integration levels of drones into current pedagogical practices. The findings highlight that drones are predominantly utilized in short-term, intermittent, and collaborative learning activities, particularly within STEM-related fields. Notably, the analysis reveals a prevalent use of drones to transform learning, mainly at the Modification and Redefinition levels of the SAMR framework. Regarding drone types, off-the-shelf drones are primarily used for applying-oriented learning and are evenly distributed across the Augmentation, Modification, and Redefinition levels. Conversely, custom-built drones are typically utilized for creating-oriented tasks and are most often associated with the highest SAMR level, i.e., Redefinition. Building upon these findings, the present work underscores the importance of addressing the novelty effect associated with drone-based learning, exploring strategies for sustaining student engagement over time, and investigating the cognitive benefits of intermittent drone use in educational settings. The collaborative nature of drone-based activities is also emphasized, calling for more process-oriented research to understand how drones influence collaborative learning. Copyright © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.