| Summary: | Purposes A Knowledge Tracing Model based on Multiple Behavior Features Embedded Memory Networks (MFKT) was proposed to fully utilize the learning and forgetting features in interaction records. The MFKT model considers both learning and forgetting behaviors in the learning process. Methods First, two major features, learning and forgetting, are extracted from the interaction records, and then the extracted learning features are embedded into the memory network by scalar crossover, while the forgetting features are embedded by vector combination, which is used to enhance the learning ability of MFKT model for the students’ interaction sequences. In addition, after different students’ answers are completed, the difference in knowledge level growth is considered and a knowledge growth layer to the original memory network is added for calculating the knowledge growth obtained from students’ responses. Conclusion Experiment results on public datasets show that MFKT is more in line with real learning patterns of students and can realize more accurate tracing of students’ knowledge status.
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