Multidimensional Exploration of Influencing Factors of College Students' Academic Performance: An Empirical Study Based on 18 Machine Learning Algorithms
This empirical study analyzes data from 145 UCI students using 18 machine learning algorithms to identify that the LightGBM model most accurately predicts academic performance, revealing that personal and school education factors (such as gender, age, part-time work, and class listening) are the primary drivers of success while family factors have a weaker impact, thereby enabling the development of an interpretable Shiny application for personalized academic interventions.