[2207.00798] Arigatō: Effects of Adaptive Guidance on Engagement and Performance in Augmented Reality Learning Environments

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 Arigatō (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.