Portrait-Based Academic Performance Evaluation of College Students from the Perspective of Big Data

Abstract

With the advent of the big data era, significant changes have taken place in every aspect of education. To effectively evaluate the academic performance of college students, this paper firstly establishes a scientific evaluation index system for student portrait. Taking the course Object-Oriented Programming as an example, the authors collected various data on the academic performance of college students. The collected data were normalized, and the weight of each evaluation index was determined through analytic hierarchy process (AHP). Next, a fuzzy evaluation model was constructed based on big data, and used to assess each dimension of college students’ academic performance. The evaluation reveals the problems of college students in learning and practice, and helps to generate the portrait of each student. The research results promote the realization of personalized education.