A Study of Precise Instructional Intervention Strategies Based on Learner Portraits

Abstract

Personalized education is a key trend in educational innovation, but traditional teaching is ineffective because it is difficult to satisfy students' individual differences. The development of big data and artificial intelligence has led to the emergence of learner profiling technology, which provides the possibility of accurate teaching, but existing research is still insufficient in terms of characterization and intervention strategies. In this study, we investigate the intervention strategies of precision teaching based on learner profiling, aiming to improve teaching quality and learning effectiveness. The study enriches the relevant theoretical system, provides new ideas for personalized teaching, and is significant for improving teaching quality and learning effects.