Analysis of Learning Quality Evaluation for University Student Courses with Process Assessment

With the progressive exploration and application of formative assessment in university pedagogy, this evaluative method has become widely adopted for appraising students' everyday learning attitudes and conditions.Drawing upon pertinent data regarding classroom learning experiences of students at a specific university, this paper employs machine learning, K-means clustering, the TOPSIS evaluation model, and the entropy weighting method to investigate the relationship between formative assessment and the quality of university student learning, culminating in the creation of an evaluation model.This model allows us to pinpoint the key factors influencing student learning attitudes and offers support for formative assessment in the university context.