Knowledge Tracing Models

Implementations of various Knowledge Tracing models in PyTorch

IMPORTANT NOTE: This repository is now archived, since I'm not working on the area anymore. Consider to use better alternatives such as pyKT.

Pre-processed Dataset

• Download Link: https://drive.google.com/file/d/1AOzqCpfxgK271mfYeUEe_sN9Aflszj2u/view?usp=sharing
• Dataset format: log files are seperated by users. Once you download and unzip each tar.gz file, there's a folder processed, and there are 5 subdirectories named from 1 to 5 for cross validation. Each subdirectory has its own train/val/test separation, where test dataset is shared by all 5 separations. Separation ratio are given in the following table. For each user, {user_id}.csv contains two columns (with headers): tag(skill_id, or question_id, which is a single positive integer) and correctness (0 or 1).

Dataset	Train:Val:Test	Link
ASSISTments2009	56:14:30	https://sites.google.com/site/assistmentsdata/home/assistment-2009-2010-data/skill-builder-data-2009-2010
ASSISTments2012	6:2:2	https://sites.google.com/site/assistmentsdata/home/2012-13-school-data-with-affect
ASSISTments2015	56:14:30	https://sites.google.com/site/assistmentsdata/home/2015-assistments-skill-builder-data
ASSISTmentsChall	6:2:2	https://sites.google.com/view/assistmentsdatamining
STATICS	56:14:30	https://pslcdatashop.web.cmu.edu/Project?id=48
Junyi Academy	6:2:2	https://pslcdatashop.web.cmu.edu/Project?id=244
KDDCup2010	6:2:2	https://pslcdatashop.web.cmu.edu/KDDCup/
EdNet-KT1	6:2:2	https://github.com/riiid/ednet

• For ASSISTments2009, ASSISTments2015, and STATICS data we use the same data (with different format) that used in this DKVMN implementation. Also, two files with same name (same user_id) but belong to different subdirectories may not coincides in this case, which actually does not important when we train & test models.
• Currently, ASSISTments2012 and KDDCup2010 dataset is not pre-processed yet.
• For EdNet-KT1, the dataset has only one split.

Usage

python main.py --num_workers=8 --gpu=0 --device=cuda --model=DKT --num_epochs=6 
--eval_steps=5000 --train_batch=2048 --test_batch=2048 --seq_size=200 
--input_dim=100 --hidden_dim=100 --name=ASSISTments2009_DKT_dim_100_100 
--dataset_name=ASSISTments2009 --cross_validation=1

Here are descriptions of arguments:

• name: name of the run. More precisely, the weight of the best model will be saved in the directory weight/{ARGS.name}/.

• gpu: number(s) of gpu.

• device: device. cpu, cuda, or others.

• base_path: the path where datasets are located.

• num_workers: number of workers for gpu training.

• dataset_name: the name of the benchmark dataset. Currently, ASSISTments2009, ASSISTments2015, ASSISTmentsChall, STATICS, Junyi, and EdNet-KT1 are available.

• model: name of the model. DKT, DKVMN, or NPA. (SAKT is not available yet)

• num_layers: number of LSTM layers, for DKT and NPA. Set to be 1 as a default value.

• input_dim: input embedding dimension of interactions, for DKT and NPA.

• hidden_dim: hidden dimension of LSTM models, for DKT and NPA.

• key_dim: dimension of key vectors of DKVMN.

• value_dim: dimension of value vectors of DKVMN.

• summary_dim: dimension of the last FC layer of DKVMN.

• concept_num: number of latent concepts, for DKVMN.

• attention_dim: dimension of the attention layer of NPA.

• fc_dim: largest dimension for the last FC layers of NPA.

• dropout: dropout rate of the model.

• random_seed: random seed for initialization, for reproducibility. Set to be 1 as default.

• num_epochs: number of training epochs.

• eval_steps: number of steps to evaluate trained model on validation set. The model weight with best performance will be saved.

• train_batch: batch size while training.

• test_batch: batch size while testing.

• lr: learning rate.

• warmup_step: warmup step for Noam optimizer.

• seq_size: length of interaction sequence to be feeded into models. The sequence whose length is shorter than seq_size will be padded.

• cross_validation: if cross_validation is 0, then the model is trained & tested only on the first dataset. If cross_validation is 1, then the model is trained & tested on all 5 splits, and give average results (with standard deviation).

Common features

• All models are trained with Noam optimizer.
• For Junyi Academy and EdNet-KT1, the model is trained & tested only on one train/val/test split, since dataset is huge enough and it takes long time for cross-validation.

DKT (Deep Knowledge Tracing)

• Paper: https://web.stanford.edu/~cpiech/bio/papers/deepKnowledgeTracing.pdf
• Model: RNN, LSTM (only LSTM is implemented)
• GitHub: https://github.com/chrispiech/DeepKnowledgeTracing (Lua)
• Performances:

Dataset	ACC (%)	AUC (%)	Hyper Parameters
ASSISTments2009	77.02 ± 0.07	81.81 ± 0.10	input_dim=100, hidden_dim=100
ASSISTments2015	74.94 ± 0.04	72.94 ± 0.05	input_dim=100, hidden_dim=100
ASSISTmentsChall	68.67 ± 0.09	72.29 ± 0.06	input_dim=100, hidden_dim=100
STATICS	81.27 ± 0.06	82.87 ± 0.10	input_dim=100, hidden_dim=100
Junyi Academy	85.4	80.58	input_dim=100, hidden_dim=100
EdNet-KT1	72.72	76.99	input_dim=100, hidden_dim=100

• All models are trained with batch size 2048 and sequence size 200.

DKVMN (Dynamic Key-Value Memory Network)

• Paper: http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p765.pdf
• Model: Extension of Memory-Augmented Neural Network (MANN)
• Github: https://github.com/jennyzhang0215/DKVMN (MxNet)
• Performances:

Dataset	ACC (%)	AUC (%)	Hyper Parameters
ASSISTments2009	75.61 ± 0.21	79.56 ± 0.29	key_dim = 50, value_dim = 200, summary_dim = 50, concept_num = 20, batch_size = 1024
ASSISTments2015	74.71 ± 0.02	71.57 ± 0.08	key_dim = 50, value_dim = 100, summary_dim = 50, concept_num = 20, batch_size = 2048
ASSISTmentsChall	67.16 ± 0.05	67.38 ± 0.07	key_dim = 50, value_dim = 100, summary_dim = 50, concept_num = 20, batch_size = 2048
STATICS	80.66 ± 0.09	81.16 ± 0.08	key_dim = 50, value_dim = 100, summary_dim = 50, concept_num = 50, batch_size = 1024
Junyi Academy	85.04	79.68	key_dim = 50, value_dim = 100, summary_dim = 50, concept_num = 50, batch_size = 512
EdNet-KT1	72.32	76.48	key_dim = 100, value_dim = 100, summary_dim = 100, concept_num = 100, batch_size = 256

• Due to memory issues, not all models are trained with batch size 2048.

NPA (Neural Padagogical Agency)

• Paper: https://arxiv.org/abs/1906.10910
• Model: Bi-LSTM + Attention
• Performances:

Dataset	ACC (%)	AUC (%)	Hyper Parameters
ASSISTments2009	77.11 ± 0.08	81.82 ± 0.13	input_dim=100, hidden_dim=100, attention_dim=100, fc_dim=200
ASSISTments2015	75.02 ± 0.05	72.94 ± 0.08	input_dim=100, hidden_dim=100, attention_dim=100, fc_dim=200
ASSISTmentsChall	69.34 ± 0.03	73.26 ± 0.03	input_dim=100, hidden_dim=100, attention_dim=100, fc_dim=200
STATICS	81.38 ± 0.14	83.1 ± 0.25	input_dim=100, hidden_dim=100, attention_dim=100, fc_dim=200
Junyi Academy	85.57	81.10	input_dim=100, hidden_dim=100, attention_dim=100, fc_dim=200
EdNet-KT1	73.05	77.58	input_dim=100, hidden_dim=100, attention_dim=100, fc_dim=200

• All models are trained with batch size 2048 and sequence size 200.

SAKT (Self-Attentive Knowledge Tracing)

• Paper: https://files.eric.ed.gov/fulltext/ED599186.pdf
• Model: Transformer (1-layer, only encoder with subsequent mask)
• Github: https://github.com/shalini1194/SAKT (Tensorflow)
• Performances:

Dataset	ACC (%)	AUC (%)	Hyper Parameters
ASSISTments2009	76.36 ± 0.15	80.78 ± 0.10	hidden_dim=100, seq_size=100, batch_size=512
ASSISTments2015	74.57 ± 0.07	71.49 ± 0.03	hidden_dim=100, seq_size=50, batch_size=512
ASSISTmentsChall	67.53 ± 0.06	69.70 ± 0.32	hidden_dim=100, seq_size=200, batch_size=512
STATICS	80.45 ± 0.13	80.30 ± 0.31	hidden_dim=100, seq_size=500, batch_size=128
Junyi Academy	85.27	80.36	hidden_dim=100, seq_size=200, batch_size=512
EdNet-KT1	72.44	76.60	hidden_dim=200, seq_size=200, batch_size=512