Knowledge Tracking Model with Forgetting, Ability and Experience
Zhijun Li, Liu Chen, Qin Xianjing, Gao Yang · 2023
The knowledge tracking model can evaluate students' knowledge level and learning status according to their learning data, and is an important technical means in the field of educational evaluation. Generally speaking, each student's learning ability is different, and learning is a process of knowledge accumulation, which will be accompanied by forgetting. These fac-tors will have an important impact on the learning effect of students. In order to accurately track students' knowledge status, a knowledge tracking model based on forgetting, ability and experience (F AEKT) is proposed. The F AEKT model uses deep neural networks to analyze and model students' learning data, fully consider the impact of forgetting on learning effects, and comprehensively consider students' learning ability and learning experience in the prediction stage of the model. The effective-ness of the F AEKT model is proved by experiments on two public datasets and comparisons with other knowledge tracking models.