Student behavior analysis and academic performance prediction based on campus big data

Abstract In this paper, college students in the digital campus environment are taken as the research object, the purpose is to collect and summarize the data sources about digital campus for the calculation of students’ multidimensional behaviors, and to prepare for the study of students’ behavior patterns at each different learning stage after quantitative calculation, and constructing a multi-feature fusion student achievement prediction model by using machine learning algorithm, In this way, some effective learning methods can be provided, which have a certain effect on how to improve students’ academic performance, learning feedback and early warning. In order to obtain the input features required by the prediction model in this paper, the quantitative behavior features are processed first to obtain the selected features before the next step can be carried out, then the regression algorithm, model parameters, model evaluation methods and metrics used in this paper. Ablation experiments are also used to explore the performance of the model and different characteristics of different regression algorithms, especially the necessity of approximate entropy and change complexity measurement in the model. The development of this research has far-reaching influence on students, teachers, university management and research in this field.