Student knowledge tracking based multi-indicator exercise recommendation algorithm

Personalized exercise recommendation was an important topic in the era of education informatization, the forgetting laws of students in the learning process were ignored by the traditional problem recommendation algorithm,which failed to fully tap the students' knowledge mastery level and the common characteristics of similar students,insufficient, could not reasonably promote students' learning of new knowledge or help students find and fill omissions. In view of the above defects, a multi-index exercise recommendation method based on student knowledge tracking was proposed, which was divided into two modules: preliminary screening and re-filtering of exercises, focusing on the novelty, difficulty and diversity of exercise recommendation. Firstly, a knowledge probability prediction（SF-KCCP） model combined with students' forgetting law was constructed to ensure the novelty of the recommended exercises. Then, students' knowledge and concept mastery level was accurately excavated based on the dynamic key-value