An Application Study of Data Science in Enhancing Time Management and Learning Efficiency Among High School Students

Abstract

This study explores the application of data science methods in optimizing time management and enhancing learning efficiency among high school students. Against the backdrop of the “Double Reduction” policy and concurrent academic anxiety, high school students urgently need to transition from a “time-based” to an “efficiencybased” learning paradigm. Inspired by “Quantified Self” and “Precision Learning” theories, this research attempts to construct a data-driven time management diagnostic and optimization model tailored for individual high school students. The author conducted a four-week data tracking study on themselves, collecting multidimensional data including study duration, subject distribution, sleep time, screen usage time, and subjective efficiency ratings. Utilizing Excel tools for data cleaning, descriptive statistics, and visualization analysis, patterns and potential issues within personal learning habits were identified, such as peak efficiency during specific time periods and the positive correlation between sleep and efficiency. Ultimately, personalized time management optimization strategies were developed based on these data insights. This study demonstrates the feasibility and effectiveness of the “quantified self” concept for high school students in self-awareness and self-optimization, proving the immense potential of data science as a “personal learning advisor” in enhancing students’ self-awareness and self-regulation capabilities.