Enhancing Deep Knowledge Tracing (DKT) Model by Introducing Extra Student Attributes
Girish Suragani, Lakshmi Narayana Pothuraju, Kamal Sandeep Reddi, W. Wilfred Godfrey · 2019
Modeling the knowledge of the student is very important in online tutoring systems. It helps the instructor to design effective exercises which vary in difficulty level and design curriculum according to the observed knowledge state of students. But modeling the student knowledge has got its own challenges. The model which uses RNNs (Recurrent Neural Networks) which has proved to be effective in modeling the student knowledge but has got its own limitations. We found that there were some features that were very important in predicting the performance of student but were ignored in the original DKT (Deep knowledge Tracing) model [1]. In this paper, we attempt to consider three more features such as No. of hints accessed, Student First Response Time and Number of Attempts when compared to DKT and use the same model as in DKT where the only change is made in the input vector we provide to the model. We also use certain techniques like dimensionality reduction by using autoencoder which helps in reducing the dimensionality as we are taking extra features into consideration.