Augmented Reality Learning Experiences: Survey of Prototype Design and Evaluation

TLDR

Augmented reality technology is mature for creating learning experiences in K‑12 educational settings. The study reviews AR applications for K‑12 and conducts a qualitative analysis to inform future AR learning experience design. The authors qualitatively analyzed design aspects of AR learning experiences, including display hardware, software libraries, content authoring solutions, and evaluation techniques. The review identified 87 AR learning experience studies, with 43 user studies and 7 effect sizes, showing a mean moderate effect size of 0.56; AR offers real‑world annotation, contextual visualization, and vision‑haptic visualization, supported by multimedia, experiential, and animate vision theories.

Abstract

Augmented reality (AR) technology is mature for creating learning experiences for K-12 (pre-school, grade school, and high school) educational settings. We reviewed the applications intended to complement traditional curriculum materials for K-12. We found 87 research articles on augmented reality learning experiences (ARLEs) in the IEEE Xplore Digital Library and other learning technology publications. Forty-three of these articles conducted user studies, and seven allowed the computation of an effect size to the performance of students in a test. In our meta-analysis, research show that ARLEs achieved a widely variable effect on student performance from a small negative effect to a large effect, with a mean effect size of 0.56 or moderate effect. To complement this finding, we performed a qualitative analysis on the design aspects for ARLEs: display hardware, software libraries, content authoring solutions, and evaluation techniques. We explain that AR incur three inherent advantages: real world annotation, contextual visualization, and vision-haptic visualization. We illustrate these advantages through the exemplifying prototypes, and ground these advantages to multimedia learning theory, experiential learning theory, and animate vision theory. Insights from this review are aimed to inform the design of future ARLEs.