Data Analysis Skills in Teaching Leadership: A Strategic Study to Improve Student Learning Outcomes and Digital Literacy

Abstract

This study examines the impact of incorporating data analysis skills—specifically, data interpretation, predictive analytics, and data-driven decision-making—on student learning outcomes and digital literacy. As technology and data increasingly influence education, it is essential for educators to possess the analytical expertise necessary to adapt their teaching methods effectively. A quantitative approach was employed, gathering data from 251 participants (100 educators and 151 students) through a structured questionnaire survey. Stratified random sampling ensured diverse representation across various educational levels. The data analysis utilised SPSS software, incorporating descriptive statistics, correlation analysis, and regression techniques. The results revealed a strong positive correlation between the proficient application of data analysis in educational contexts and enhanced student performance and capabilities. Predictive analytics emerged as the most significant factor in forecasting student success, with instruction in data interpretation and data-driven decision-making also yielding substantial improvements in learning outcomes. The study highlights the critical need for teacher training in data literacy and advocates for the integration of digital tools to effectively monitor student progress. The findings underscore the importance of educational leadership in utilising data to create technology-enhanced environments that support personalised learning. By offering recommendations for data-informed teaching practices, this research contributes to the development of educational policies aimed at improving academic performance and digital literacy.