Implementing Cloud-Based Feedback to Facilitate Scalable Psychomotor Skills Acquisition

Personalized feedback in psychomotor training often involves the use of sophisticated Machine Learning (ML)-based algorithms, requiring the utilization of cloud-based computational power for efficiency and scalability. By incorporating a cloud-based feedback system, learners may receive individualized feedback on their psychomotor performance in real-time or as summative analysis, allowing them to develop their abilities more efficiently. The integration of a cloud-based feedback mechanism into immersive learning environments is explored to improve the acquisition of psychomotor abilities. The essential components of the feedback system are discussed in this article, including data collection, analysis, and dissemination, as well as the obstacles and issues related to its implementation. © 2023 Copyright for this paper by its authors.