Financial Implications of Implementing a Model-Driven Approach for Dance Teaching Management Integration

Abstract

In today's dynamic educational landscape, the integration of technology into various domains has become imperative for enhancing efficiency and user experience. This study delves into the financial implications of implementing a model-driven approach for the integration of dance teaching management information systems. As dance education institutions increasingly adopt innovative technologies to streamline administrative processes and elevate the learning experience, understanding the financial considerations associated with such implementations is of paramount importance. This research investigates the multifaceted financial aspects that arise during the development, deployment and ongoing maintenance of the proposed integration system. Initial investment costs encompassing software development, interface design, infrastructure enhancement, and user training are meticulously examined. Operational expenses, including software updates, technical support, and training-related expenditures, are assessed in light of their impact on the institution's financial healthy. Finally, the deep neural network is used to grade students' oral assignments. The findings of the study suggest that using a recurrent neural network could increase translation accuracy. Speech recognition may be improved by employing deep neural networks and recurrent neural networks. Students' oral tasks can be guided and scored using deep neural networks. The usage of deep neural networks and recurrent neural networks can help to increase the efficiency of English instruction and, as a result, improve students' English proficiency.