Arigatō: effects of adaptive guidance on engagement and performances in augmented reality learning environments

Anuradhi Maheshya Weerasinghe, Aaron John Quigley, Klen Copic Pucihar, Alice Toniolo, Angela Ruth Miguel, Matjaz Kljun

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
7 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)3737 - 3747
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number11
Early online date1 Sept 2022
Publication statusPublished - Nov 2022


  • Experimental learning
  • Instructional guidance
  • Adaptive learning systems
  • Augmented reality
  • Engagement
  • Language learning


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