Aligning open educational resources to new taxonomies: How AI technologies can help and in which scenarios
This repo contains the code for the paper:
Zhi Li, Zachary A Pardos, and Cheng Ren. (In press). Aligning Open Educational Resources to New Taxonomies: How AI technologies can help and in which scenarios. Computers & Education.
The code is written and run with the following packages:
- Python 3.6.8
- PyTorch 1.7.1+cu101
- NumPy 1.18.5
- pandas 1.1.5
- scikit-learn 0.24.2
- sentence-transformers 1.2.1
- efficientnet-pytorch 0.7.0
data/contains the Common Core Mathematics skills list and text embeddings of the skill descriptions.data/examplescontains two example problems for demostration purpose.
Put the problem texts and images in a folder similar to data/examples. Put training labels if any in a csv file with two columns source and target.
Prepare the problems data and labels as described above. Use TextEncoder, ImageEncoder, and Fusion in encoder/ to encode problem data into embeddings, then use ClassificationModelTrainer and SimilarityMatchingModelTrainer in training.py to train the models.
Prepare the problems data as described above. Use TextEncoder, ImageEncoder, and Fusion in encoder/ to encode problem data into embeddings, then use ClassificationModelTester and SimilarityMatchingModelTester in inference.py to run the models.
We also release two pre-trained models that map problems to Common Core skills in models/, and provide a demo.ipynb to show how to use the pre-trained models to predict the skills for the given problems.
We referenced the following repos for the code: