Arigatō : effects of adaptive guidance on engagement and performances in augmented reality learning environments
MetadataShow full item record
Experiential learning (ExL) is the process of learning through experience or more specifically “learning through reflection on doing”. In this paper, we propose a simulation of these experiences, in Augmented Reality (AR), addressing the problem of language learning. Such systems provide an excellent setting to support “adaptive guidance”, in a digital form, within a real environment. Adaptive guidance allows the instructions and learning content to be customised for the individual learner, thus creating a unique learning experience. We developed an adaptive guidance AR system for language learning, we call Arigato (Augmented Reality Instructional ¯ Guidance & Tailored Omniverse), which offers immediate assistance, resources specific to the learner's needs, manipulation of these resources, and relevant feedback. Considering guidance, we employ this prototype to investigate the effect of the amount of guidance (fixed vs. adaptive-amount) and the type of guidance (fixed vs. adaptive-associations) on the engagement and consequently the learning outcomes of language learning in an AR environment. The results for the amount of guidance show that compared to the adaptive-amount, the fixed-amount of guidance group scored better in the immediate and delayed (after 7 days) recall tests. However, this group also invested a significantly higher mental effort to complete the task. The results for the type of guidance show that the adaptive-associations group outperforms the fixed-associations group in the immediate, delayed (after 7 days) recall tests, and learning efficiency. The adaptive-associations group also showed significantly lower mental effort and spent less time to complete the task.
Weerasinghe , A M , Quigley , A J , Copic Pucihar , K , Toniolo , A , Miguel , A R & Kljun , M 2022 , ' Arigatō : effects of adaptive guidance on engagement and performances in augmented reality learning environments ' , IEEE Transactions on Visualization and Computer Graphics , vol. 28 , no. 11 , pp. 3737 - 3747 . https://doi.org/10.1109/tvcg.2022.3203088
IEEE Transactions on Visualization and Computer Graphics
Copyright © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/TVCG.2022.3203088.
DescriptionFunding information: This research was supported by European Commission through the InnoRenew CoE project (Grant Agreement 739574) under the Horizon2020 Widespread-Teaming program and the Republic of Slovenia (investment funding of the Republic of Slovenia and the European Union of the European Regional Development Fund). We also acknowledge support from the Slovenian research agency ARRS (program no. BI-DE/20-21-002, P1-0383, J1-9186, J1-1715, J5-1796, and J1-1692).
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.