Improving Deep Knowledge Tracing with Prediction-Consistent Regularization

MPhil Thesis Defence


Title: "Improving Deep Knowledge Tracing with Prediction-Consistent 
Regularization"

By

Mr. Chun Kit YEUNG


Abstract

Knowledge tracing is one of the key research areas for empowering 
personalized education. It is a task to model student’s knowledge state, 
i.e., the mastery level of a knowledge component, based on their 
historical learning trajectories. In recent years, a recurrent neural 
network model called deep knowledge tracing (DKT) has been proposed to 
handle the knowledge tracing task. Literature has shown that DKT generally 
outperforms traditional methods. However, through our extensive 
experimentation, we have noticed two major problems, which would mislead 
the interpretation of student’s knowledge state, in the DKT model. 
Firstly, the model fails to reconstruct the knowledge state with respect 
to the observed input, and secondly, the predicted performance of student 
across time-steps is not consistent. In this thesis, we introduce 
regularization terms that correspond to reconstruction and waviness to the 
loss function of the original DKT model to enhance the consistency in 
prediction, and evaluate how the regularized DKT model (DKT+) relieves 
these two problems. Furthermore, the DKT+ model is employed to build 
predictive models that predict whether the first job of a student out of 
college belongs to a STEM (the acronym for science, technology, 
engineering, and mathematics) field. Experiments show that the DKT+ model 
effectively alleviates the two problems and improves the prediction 
accuracy of STEM predictors, compared to the original DKT model.


Date:			Thursday, 2 August 2018

Time:			2:30pm - 4:30pm

Venue:			Room 3494
 			Lifts 25/26

Committee Members:	Prof. Dit-Yan Yeung (Supervisor)
 			Dr. Raymond Wong (Chairperson)
 			Dr. Brian Mak


**** ALL are Welcome ****