Influence Mechanism of Educational Leadership on Environmental Accounting Based on Big Data Algorithm

Over the last 20 years, big data techniques in teaching have been overgrown. Making educational decisions now includes information knowledge as a crucial component. This started a trend for using big data algorithms strategically. Technological advances have been used to analyze the enormous amount of information and develop strategic judgments. The current study aims to address issues with the conventional instructional, administrative management solution focused on manual rule formulation in big data storage and interpretation and has poor efficiency in analyzing big data and lacking value in developing education leadership qualities. The study suggests an educational leadership model based on big data algorithm (ELM-BDA) to explore the student leadership performance that relies on cooperative filtration and fuzzy C-means (FCM) and big data. The different influencing mechanisms and factors directly linked to educational leadership were also analyzed using a big data algorithm. To build an intelligent institutional administrative system, the research also exposes it to organizational studies. By evaluating the big data research already in existence, this study emphasizes the expanding significance of big data. Additionally, this study explores the effects of big data analytics on educational leadership qualities by utilizing an FCM. A scoring system is designed to predict the student's leadership level, and using the big data algorithms, the students are motivated and trained to improve their skills. The education and learning method can be enhanced at educational institutions through better decision-making to use this big data for leadership development. Big data facilitates efficient educational decision-making by merging various data and telecommunications technologies. Using big data in schooling will increase—leadership quality among students. To effectively use big data for decision-making, academic leaders must create new types of learning and monitoring systems.