Research on the Application of Information Grain-based Affective Computing Model in College Students' Psychoeducation

Abstract

Aiming at the problems of gradual curricularization of college students' psychological education and poor targeting of teaching content, this paper establishes an emotional calculation model to understand the key factors of college students' emotional needs, emotional type and intensity, and emotional classification. The definition and optimization method of information granularity are clarified to calculate the emotional factors. Meanwhile, the selection of emotional need variables and related assumptions are introduced, and the validity and applicability of the emotional factors are verified using multiple linear regression. The results showed that in terms of mental health teaching effectiveness, the emotion calculation model based on information granules improved by 19.5%, and the control group that did not use the emotion calculation model had a relatively weak teaching effectiveness in psychoeducation with a T-value less than 1. Three of the experimental groups' psychological test scores reached 10 points, and by deeply exploring the emotional factors they are expected to provide college students with more personalized and comprehensive psychoeducational support to promote their mental health and overall development.