PREREQ-IAAI-19

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Inferring Concept Prerequisite Relations from Online Educational Resources (IAAI-19)

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#### This is the source code for the paper [**"Inferring Concept Prerequisite Relations from Online Educational Resources"**](https://arxiv.org/abs/1811.12640), Sudeshna Roy, Meghana Madhyastha, Sheril Lawrence, Vaibhav Rajan. 31st AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-19). If you use any part of this code cite this paper.

#### [**Oral Presentation**](https://github.com/suderoy/PREREQ-IAAI-19/blob/master/PREREQ_presentation.pdf)

#### First run the pairwise Link LDA

to learn the /beta matrix (concept vectors). This code is modfied from original LDA code.

cd "pairwise link lda"
    make

./lda est 0.01 100 settings.txt "../datasets/NPTEL MOOC Dataset/bow_feature.txt" "../datasets/NPTEL MOOC Dataset/prerequisites.link" random "../datasets/NPTEL MOOC Dataset/k100_a0.01"

#### Train the siamese network

It does a 10 fold cross-validation on the data.

cd "siamese network"
    python siamese_fc_relu.py  "NPTEL MOOC Dataset" "../datasets/NPTEL MOOC Dataset/k100_a0.01 100"

*You may change all the above occurence of "NPTEL MOOC Dataset" to "University Course Dataset" to run on University Course Dataset.*

#### To run on your own dataset:

1. Have the concept vocabulary in a text file as "vocab.txt"

2. Have all the files in the raw data ready. The files are:

- *cs_courses.csv*: These are CS-related course information. Each line is formatted as "\<Course_id\>,\<Course_description\>". Note the course titles are located at the begining of the description.

- *cs_edges.csv*: There are course prerequisite information. Each line "\<course_1\>,\<course_2\>" represents \<course_2\> is a prerequisite for \<course_1\>.
  
- *cs_preqs.csv*: These are concept prerequisite pairs. Each line "\<Concept_A\>,\<Concept_B\>" represents the prerequisite relationship.

3. Mention the paths of "cs_preqs.csv" and "vocab.txt" in "data preprocessing/preq_preparation.py" and  run which writes the 'concept_vocab.pkl' in  the same directory.

python preq_preparation.py
       
   Add the paths of "cs_course.csv" and "concept_vocab.pkl" in "data preprocessing/create_bow_features.py". Then run

python create_bow_features.py

4. Run the pairwise Link LDA with correct paths as explained earlier.

5. Have the train-test splits ready and mention the data paths in the "siamese network/data_processing/siamese_data_train_test.py

6. Run the siamese network as explained above.

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