kt-algos

Simple and performant implementations of knowledge tracing algorithms:

• DAS3H
• DKT
• SAKT

Setup

In a new conda environment with python 3, install PyTorch and the remaining requirements:

pip install -r requirements.txt

The code supports the following datasets:

• ASSISTments 2009-2010 (assistments09)
• ASSISTments 2012-2013 (assistments12)
• ASSISTments 2015 (assistments15)
• ASSISTments Challenge 2017 (assistments17)
• Bridge to Algebra 2006-2007 (bridge_algebra06)
• Algebra I 2005-2006 (algebra05)

Dataset	# Users	# Items	# Skills	# Interactions	Mean # skills/item	Timestamps	Median length
assistments09	3,241	26,634	124	397,137	1.20	No	26
assistments12	29,018	53,086	265	2,711,602	1.00	Yes	49
assistments15	14,567	100	100	658,887	1.00	No	20
assistments17	1,708	3,162	102	942,814	1.23	Yes	441
bridge_algebra06	1,146	129,263	493	1,817,476	1.01	Yes	1,362
algebra05	574	173,113	112	607,025	1.36	Yes	574

To use a dataset, download the data from one of the links above and place the main file under data/<dataset codename>/data.csv if it is an ASSISTments dataset and under data/<dataset codename>/data.txt otherwise. To preprocess a dataset:

python prepare_data.py --dataset <dataset codename> --remove_nan_skills

Training

Logistic regression

To encode a sparse feature matrix with specified features:

python encode_lr.py --dataset <dataset codename> --items --skills --wins --attempts --time_windows

To train a logistic regression model with a sparse feature matrix encoded through encode_lr.py:

python train_lr.py data/<dataset codename>/X-lr-iswa_tw.npz --dataset <dataset codename>

Feedforward neural network

To encode a sparse feature matrix with specified features:

python encode_ffw.py --dataset <dataset codename> --total --items --num_prev_interactions=1

To train a feedforward neural network model with a dense feature matrix encoded through encode_ffw.py:

python train_ffw.py data/<dataset codename>/X-ffw-ti-1.npz --dataset <dataset codename>

Deep knowledge tracing

To train a DKT model:

python train_dkt.py --dataset <dataset codename> --embed_inputs

Self-attentive knowledge tracing

To train a SAKT model:

python train_sakt.py --dataset <dataset codename> --embed_inputs 

Results

Algorithm	assistments09	assistments12	assistments15	assistments17	bridge_algebra06	algebra05
IRT	0.69	0.71	0.64	0.68	0.75	0.77
PFA	0.77	0.75	0.70	0.71	0.80	0.83
DAS3H	-	0.75	-	0.72	0.79	0.83
FFW	0.78		0.71	0.71
DKT
SAKT

Legend for results in table:

• IRT: logistic regression with --item flags
• PFA: logistic regression with --item --skills --wins --attempts flags
• DAS3H: logistic regression with --item --skills --wins --attempts --time_windows flags
• FFW: feedforward neural network with --total --items --num_prev_interactions=1 flags