Artificial intelligence prediction of students' grades and personalized student training mechanism based on campus behavior sequence modeling

Abstract

Academic performance is the most important and basic indicator to evaluate students' learning. Analysis of academic performance can help teachers master students' learning styles and specific states, so this is conducive to the diversity of teachers' teaching and to the individual situation of students. In order to better analyze the actual situation of students, this paper chooses artificial intelligence technology as technical support to build a prediction system of student behavior and performance. Secondly, based on the existing campus big data system, the student behavior characteristic data is extracted. After further optimizing the data, a dynamic evolution method based on student behavior sequence modeling is established, and an algorithm for predicting and evaluating student performance is developed. Finally, based on the behavioral data of 20 2020 students in a university from the autumn of 2021 to the spring of 2022, including life rules, consumption levels, social relations, entering the library and choosing public basic courses, this paper examines the prediction results of artificial intelligence on student performance. The results show that the system designed in this paper can build a performance prediction model through data mining technology, establish a personalized student training mechanism, and achieve the purpose of predicting student performance according to students' daily behavior, so as to improve students' academic performance. By studying the behavior sequence modeling in campus field, this paper designs a kind of student achievement prediction system using artificial intelligence, so as to better assist teachers in teaching.