Models
Since the deep learning based KT models can be categorized into deep sequential models, memory augmented models, adversarial based models, graph based models and attention based models in our work, we mantle the KT models in these four categories in pyKT.
Models |
Category |
---|---|
AKT |
Attention |
ATKT |
Adversarial |
DKT-Forget |
Sequential |
DKT |
Sequential |
DKT+ |
Sequential |
DKVMN |
Memory |
GKT |
Graph |
HawkesKT |
Neural Network |
IEKT |
Sequential |
KQN |
Sequential |
LPKT |
Sequential |
SAINT |
Attention |
SAKT |
Attention |
SKVMN |
Memory |
AKT

ATKT
Adversarial training (AT) based KT method (ATKT) is an attention based LSTM model which apply the adversarial perturbations into the original student interaction sequence to reduce the the risk of DLKT overfitting and limited generalization problem.

DKT-Forget
DKT-Forget explores the deep knowledge tracing model by considering the
forgetting behavior via incorporate multiple forgetting information.

DKT
DKT is the first model that uses Recurrent Neural Networks (RNNs) to solve Knowledge Tracing.

DKT+
DKT+ introduces regularization terms that correspond to reconstruction and waviness to the loss function of the original DKT model to enhance the consistency in KT prediction.
DKVMN
Dynamic key-value memory networks (DKVMN) exploit the relationships between latent KCs which are stored in a static memory matrix key and predict the knowledge mastery level of a student directly based on a dynamic memory matrix value.

GKT
Graph-based Knowledge Tracing (GKT) is a GNN-based knowledge tracing method that use a graph to model the relations between knowledge concepts to reformulate the KT task as a time-series node-level classification problem.

HawkesKT
HawkesKT is the first to introduce Hawkes process to model temporal cross effects in KT.

IEKT
Individual Estimation Knowledge Tracing (IEKT) estimates the students’ cognition of the question before response prediction and assesses their knowledge acquisition sensitivity on the questions before updating the knowledge state.

KQN
KQN uses neural networks to encode student learning activities into knowledge state and skill vectors, and calculate the relations between the interactions via dot product.

LPKT
Learning Processconsistent Knowledge Tracing(LPKT) monitors students’ knowledge state by directly modeling their learning process.

SAINT
Separated Self-AttentIve Neural Knowledge Tracing(SAINT) is a typical Transformer based structure which embeds the exercises in encoder and predict the responses in decoder.

SAKT
Self Attentive Knowledge Tracing (SAKT) use self-attention network to capture the relevance between the KCs and the students’ historical interactions.

SKVMN
This model unifies the strengths of recurrent modeling capacity and the capability of memory networks to model the students’ learning precocesses.

qDKT
qDKT(base) is a model same as DKT, but use the question ID as the input.
Deep-IRT
Deep-IRT is a synthesis of the item response theory (IRT) model and a knowledge tracing model that is based on the deep neural network architecture called dynamic key-value memory network (DKVMN) to make deep learning based knowledge tracing explainable.
