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Dynamic Key-Value Memory Networks With Rich Features for Knowledge Tracing
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DOI：10.1109/TCYB.2021.3051028.png)
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Abstract
En 中文
Knowledge tracing is an important research topic in student modeling. The aim is to model a student's knowledge state by mining a large number of exercise records. The dynamic key-value memory network (DKVMN) proposed for processing knowledge tracing tasks is considered to be superior to other methods. However, through our research, we have noticed that the DKVMN model ignores both the students' behavior features collected by the intelligent tutoring system (ITS) and their learning abilities, which, together, can be used to help model a student's knowledge state. We believe that a student's learning ability always changes over time. Therefore, this article proposes a new exercise record representation method, which integrates the features of students' behavior with those of the learning ability, thereby improving the performance of knowledge tracing. Our experiments show that the proposed method can improve the prediction results of DKVMN.
Keywords:
Predictive models
Prediction algorithms
Task analysis
Clustering algorithms
Sun
Knowledge engineering
Error analysis
Dynamic key-value memory network (DKVMN)
knowledge tracing
student clustering
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