TOWARDS INTERPRETABLE NEURAL KNOWLEDGE TRACING MODELS

MPhil Thesis Defence


Title: "TOWARDS INTERPRETABLE NEURAL KNOWLEDGE TRACING MODELS"

By

Mr. Jinseok LEE


Abstract

Knowledge Tracing (KT) has become an important research problem in the 
educational community with the rise of online learning environments, 
including intelligent tutoring systems. In this thesis, the two tasks of 
the KT problem, description and prediction, are identified, and the 
advantages and disadvantages of existing neural and non-neural KT models 
are reviewed from the perspectives of those KT tasks. Unfortunately, 
existing KT models did not excel at both tasks but either showed high 
prediction performance or provided intuitive description of students.

The Knowledge Query Networks (KQN) model is proposed to provide intuitive 
and descriptive knowledge state of students while achieving the 
state-of-the-art performance for the prediction task. The architecture of 
KQN leads to the a distance measure between skills where the distance 
could be interpreted from the probabilistic perspective. Experiments show 
that KQN performs better than the previous state-of-the-art KT models for 
the prediction task while having descriptive states of students. 
Additionally, through an ablation study, KQN is proven to be stable in 
learning model parameters.

Additionally, the Controlled Deep Knowledge Tracing (ConDKT) model is 
proposed in an effort to guarantee consistent probability transitions with 
the aid of constrained neural networks. Particularly, a novel variant of 
Long Short-Term Memory is proposed, which controls the movement of hidden 
states with respect to the input features at current time step. Through 
experiments, ConDKT is shown to be comparable to Deep Knowledge Tracing 
(DKT) for prediction while having explainable network output as opposed to 
DKT, which has been pointed out by literature for its lack of 
interpretability.


Date:			Monday, 12 August 2019

Time:			2:30pm - 4:30pm

Venue:			Room 4472
 			Lifts 25/26

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


**** ALL are Welcome ****