# 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.