Paper: https://arxiv.org/pdf/1908.02146.pdf

Code: https://github.com/JSLBen/Knowledge-Query-Network-for-Knowledge-Tracing

San Diego State University

1. Introduction
2. Related Work
3. Our Proposed Model
4. Probabilistic Skill Similarity
5. Experiments
6. Results and Analysis
7. Conclusions and Future Work


Knowledge Query Network for Knowledge Tracing

There are two types of a learner models:

1. Domain Model
This types of models try to associate skills to a particular task and find out the structure of skill domain. 

2. Knowledge Model
This types of model observe how the student knowledge changes while he/she is practicing. This models are also referred as Knowledge Tracing models. In order KT models to have a good performance it is quintessential for them to apt in describing knowledge state of the student and to be able to correctly classify whether student will answer certain problem correctly.

As it was described in the paper current KT models lack the ability to describe knowledge state or knowledge interaction (interaction of student knowledge state at time t and skill at time t+1). In order to solve these issues the authors create a new model called Knowledge Query Network (KQN)  



Introduction (Knowledge Query Network for Knowledge Tracing)

1. Bayesian Based Knowledge Tracing
2. Factory Analysis Based Knowledge Tracing
3. Deep Learning Based Knowledge Tracing

Knowledge Tracing (Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory)

1. Motivation
2. Objective
3. Architecture Overview
4. Inputs
5. Knowledge State Query
6. Knowledge State Encoder
7. Skill Encoder
8. Optimization

Our Propose Model (Knowledge Query Network for Knowledge Tracing)

1. Distance measures for skill vectors:

$$d_{Euclidean} (s_1, s_2) ^2 = ||s_1 - s_2||^2 = 2(1- s_1\cdot s_2) = 2d_{cosine}(s_1, s_2) , \forall s_1, s_2 \in U^d $$

2. Distances and Odds Ratios

Then the authors "show how a pairwise distance between two skill vectors is related to the logarithm of their odds ratio":
$$p = P( c = 1 | KS, s), o =\frac {p} {1-p}$$ 
$$ \log (\frac {o_1}{o_2} ) ^2 = (KS \cdot \delta_{1, 2} )^2 \times 2dcosine(s_1, s_2)$$
$$\delta_{1, 2} = \frac {s_1 - s_2} {|| s_1 - s_2||}$$

Probabilistic Skill Similarity (Knowledge Query Network for Knowledge Tracing)

Experiments (Knowledge Query Network for Knowledge Tracing)

Reference for Knowledge Query Network for Knowledge Tracing

1. Correctness Prediction
2. Knowledge Interaction Visualization
3. Skill Domain Analysis
4. Sensitivity Analysis of the Vector Space Dimensionality

Results and Analysis (Knowledge Query Network for Knowledge Tracing)

1. KQN performs better than all the previous models.

2. KQN enables to query student knowledge state with respect to different skills.

3. KQN architecture leads to the concept of probabilistic skill similarity which is helpful for the domain modeling and clustering.

4. KQN is resistant towards changes in the variability of the dimensionality of the vector space for knowledge state and skill vectors.

KQN has a lot more parameters than neural networks and it is computationally heavier than the DKT. 
As it was mentioned in the paper, skill distances are "useful features for domain
modeling to explore the latent structure of the skill domain, which can be a future direction of the KT research"

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