AGILEST Approach: Using Machine Learning Agents to Facilitate Kinesthetic Learning in STEM Education through Real-time Touchless Hand Interaction

ABSTRACT

There is an increasing interest in creating interactive learning applications using innovative interaction technologies, especially in STEM (Science, technology, engineering, and mathematics) subjects. Recent developments in machine learning have allowed for nearly perfect hand-tracking recognition, introducing a touchless modality for interaction within Augmented Reality(AR) environments. However, the research community has not explored the pedagogical approach of Kinesthetic Learning or “Learning by Doing”, hand tracking, and machine learning agents combined with Augmented Reality technology. Fundamentally, this exploration of touchless interaction technologies has taken on new importance in the new post-COVID world. Meanwhile, machine learning has gained attention for its ability to enhance personalized learning and play a vital new role as a virtual instructor. This paper proposes a novel approach called the AGILEST approach, which uses machine learning Agents to facilitate interactive kinesthetic learning in STEM education through touchless interaction. The first case study for this approach will be an AR learning application for chemistry. This application uses real-time touchless hand interaction for kinesthetic learning and uses a machine learning agent to act as both trainer and assessor of the user. The evaluation of this research has been conducted remotely through a usability study with expert reviewers, which includes 15 young researchers with peer-reviewed work in Human-Computer Interaction & AR and 2 subject experts STEM teachers at the secondary school level. The usability evaluation through NASA Task Load Index (NASA-TLX), Perceived Ease of Use(PUEU), and Perceived Usefulness(PU) with expert reviewers provide positive feedback about this approach for productive learning gain, engagement and interactiveness in learning STEM subjects.