AUGMENTED REALITY SYSTEMS AND USER INTERACTION TECHNIQUES FOR STEM LEARNING

Abstract

Learning practices and crosscutting concepts in science, technology, engineering, andmathematics (STEM) subjects pose challenges to young learners. Without external support to foster long-term interest and scaffold learning, children might lose interest in STEM subjects. While prior research has investigated how Augmented Reality (AR) may enhance learning of scientific concepts and increase student engagement, only a few considered young children who require developmentally appropriate approaches. The primary goal of my dissertation is to design, develop, and evaluate AR learning systems to engage children (ages 5-11) with STEM experiences. Leveraging advanced computer vision, machine learning, and sensing technologies, my dissertation explores novel user interaction techniques. The proposed techniques can give learners chance to investigate STEM ideas in their own setting, what educators call contextual learning, and lower barriers for STEM learning practices. Using the systems, my research further investigates Human-Artificial Intelligence (AI) interaction—how children understand, use, and react to the intelligent systems. Specifically, there are four major objectives in my research including: (i) gathering design ideas of AR applications to promote children’s STEM learning; (ii) exploring AR user interaction techniques that utilize personally meaningful material for learning; (iii) developing and evaluating AR learning systems and learning applications; and (iv) building design implications for AR systems for education.