Unsupervised Deep Autoencoders for Feature Extraction with Educational Data
This repository contains the code for the paper (see
bosch-dlwed17-camera.pdf) presented at the
Deep Learning with Educational Data workshop at the
2017 Educational Data Mining conference.
Bosch, N., & Paquette, L. (2017). Unsupervised deep autoencoders for feature extraction with educational data. In Deep Learning with Educational Data Workshop at the 10th International Conference on Educational Data Mining.
The code was tested with Keras 2.0.3 and Tensorflow 1.1.0 neural network libraries.
Data were from Betty's Brain. These data are required for the code to run, and are not publicly available. However, the code could be (relatively) easily adapted to another dataset.
Model building generally consists of data preprocessing, autoencoder feature extraction, and supervised learning phases.
preprocess_bromp.py- takes raw BROMP files created by the HART application and combines them into an easily-used format
preprocess_timeseries.py- creates timeseries (evenly spaced in time) data from Betty's Brain interaction logs
preprocess_seq.py- creates sequences suitable for training RNN models from the timeseries data; sequences are saved to numpy binary files for faster loading later
Autoencoder feature extraction
ae_lstm.py- this and similar files (e.g.,
vae_lstm.py) trains the autoencoders
extracy_embeddings.py- takes a trained model, feeds in data sequences, and saves the embeddings generated by the model to be used as features for supervised models
align_embeddings+labels.py- matches up BROMP affect/behavior labels to the embeddings extracted from a model, saving only the rows with labels to create a file with features and labels which can be used for supervised learning
supervised/ae_feats_test.py- trains a decision tree (CART) model with the autoencoder features
supervised/expert_feats_extract.py- extracts some simple features with the traditional method (manual design by experts) of feature extraction for model building
supervised/expert_feats_test.py- builds a model using the expert features to serve as a baseline
visualize_activations.py generates images of model activations by feeding in a random subset of
samples to a trained autoencoder and creating histograms of the activations of every layer in the
network. For layers with several neurons (> 15), a subset of neurons is sampled to create a more
The model structure is also visualized (requires the