Predicting Student Learning Effectiveness in Higher Education Based on Big Data Analysis

With the global higher education entering the era of “quality is king,” the perception and acquisition of learning experience and the evaluation of learning quality and its effect have attracted more and more attention. However, at present, most of the academic evaluations of university courses are based on final examinations, supplemented by appropriate amount of regular tests, and little attention is paid to the development and quality of students’ learning ability in the learning process, which inevitably causes academic evaluations to be equivalent to assessments and deviates from the original purpose of academic evaluations to promote continuous improvement of teaching and learning quality. On this basis, this study uses the big data analysis method to predict the teaching effect of college students, in order to improve the existing teaching problems, grasp the teaching essence, and construct a relatively perfect curriculum evaluation system combined with the course of “teaching effect guidance + teaching action,” so as to further improve the academic evaluation system and improve the teaching quality. This will provide a reference for further improving the academic evaluation system and improving the teaching quality.