Meta Transfer Learning for Early Success Prediction in MOOCs

This repository is the official implementation of the L@S 2022 Paper entitled "Meta Transfer Learning for Early Success Prediction in MOOCs" written by Vinitra Swamy, Mirko Marras, and Tanja Käser.

Experiments are located in scripts/, corresponding directly to the experimental methodology mentioned in the paper. At the beginning of each script, you will find the research question this experiment targets and a short objective statement regarding the model the script generates. For more information about each experiment, please reference the paper directly.

The best behavior-only (BO), behavior-timewise-meta (BTM), and behavior-static-meta (BSM) models for each early prediction setting (40% and 60%) can be found in models/. These models can be used to warm-start downstream student performance predictions for new or ongoing courses.

Usage guide

0. Install relevant dependencies with pip install -r requirements.txt.

1. Extract relevant features sets (BouroujeniEtAl, MarrasEtAl, LalleConati, and ChenCui) through our lab's EDM 2021 contribution on benchmarks for feature predictive power. Place the results of these feature extraction scripts in data/. A toy course example of extracted features is included in the data/ folder.

2. Fill out the data/metadata.csv file with relevant meta information about your course(s). One example of course metadata is currently showcased in the file. This information is necessary for extracting the meta features in the BTM and BSM models.

3. Run your desired experiment from scripts/ by executing the script with Python 3.7 or higher.

Models

We present three model architectures to predict early pass / fail student performance prediction. The features used as input to each of these models is truncated to 40% or 60% of the duration of a course, in order to simulate downstream intervention for an ongoing course.

• Behavior Only (BO): Models trained only using features about student behavior.
• Behavior + Time-wise Meta (BTM): Models trained using behavior features and meta features, combined at each timestep and used together as model input.
• Behavior + Static Meta (BSM): Models trained using behavior and meta features, combined statically at different layers of the model with attention and projection.

The best models of each architecture for the two early prediction levels (40% and 60%) are showcased in the models/ folder, and can be produced with the BO_Nto1_Diff.py, BSM_Nto1_Diff.py, and BTM_Nto1_Diff.py scripts respectively.

You can load a model and compute predictions (inference) with the following code snippet:

pretrained_model = 'BO_Nto1_Diff_0.4_lstm-bi-64-baseline_best_bidirectional_lstm_64_ep0.4_1641513647.8297'
model = tf.keras.models.load_model('../models/' + pretrained_model)
predictions = model.predict(features)

Scripts

We extensively evaluate our models on a large data set including 26 MOOCs and 145,714 students in total, with millions of student interactions. With our analyses, we target the following three research questions, addressed through experiments in this repository:

• RQ 1: Can student behavior transfer across iterations of the same course and across different courses?

  • BO_1to1.py: Train a model on one course's behavior features (behavior-only) and predicting on one course's behavior features.
  • BO_Nto1_Diff.py: Train a model on N courses' behavior features (behavior-only) and predicting on one course's (or multiple held out courses') behavior features.
  • BO_Nto1_Same.py: Train a model on behavior features (behavior-only) from previous iterations of a course and predict on behavior features from that course's current iteration.

• RQ 2: Is a meta learning model trained on a combination of behavior and course metadata information more transferable?

  • BSM_Nto1_Diff.py: Train a model on behavior and meta features from N courses, combined statically (with attention and projection), predict on one course (or multiple hold-out courses). This script produces the final architecture, using FastText encoding.
  • BTM_Nto1_Diff.py: Train a model on behavior and meta features from N courses, combined at each timestep, predict on one course (or multiple hold-out courses).
  • Encoding experiments: encoding the course title and description text using different encoders.
    • BTM_Nto1_Diff_FastText.py: Encode textual meta features with FastText, in a BTM model.
    • BTM_Nto1_Diff_SentenceBERT.py: Encode textual meta features with SentenceBERT, in a BTM model.
    • BTM_Nto1_Diff_UniversalSentEncoder.py: Encode textual meta features with SentenceBERT, in a BTM model.

• RQ 3: Can fine-tuning a combined model on past iterations of an unseen course lead to better transferable models?

  • BO_Nto1_Diff_Finetune.py: Finetune the model trained in BO_Nto1_Diff.py on one course.
  • BO_NtoC_Diff_Finetune.py: Finetune the model trained in BO_Nto1_Diff.py on previous iterations of a course, predict on current iteration.
  • BSM_NtoC_Diff_Finetune.py: Finetune the model trained in BSM_Nto1_Diff.py on previous iterations of a course, predict on current iteration.
  • BTM_NtoC_Diff_Finetune.py: Finetune the model trained in BTM_Nto1_Diff.py on previous iterations of a course, predict on current iteration.

• Helper Utilities

  • predict_on_all_students.py: Evaluate a trained model on all students, not just the subset of hard students. This code can produce the results showcased in Figure 4.
  • rnn_models.py: Bi-LSTM model architectures used in BO and BTM experiments.

Contributing

This code is provided for educational purposes and aims to facilitate reproduction of our results, and further research in this direction. We have done our best to document, refactor, and test the code before publication.

If you find any bugs or would like to contribute new models, training protocols, etc, please let us know. Feel free to file issues and pull requests on the repo and we will address them as we can.

Citations

If you find this code useful in your work, please cite our paper:

Swamy, V., Marras, M., Käser, T. (2022). 
Meta Transfer Learning for Early Success Prediction in MOOCs. 
In: Proceedings of the 2022 ACM Conference on Learning at Scale (L@S 2022). 

License

This code is free software: you can redistribute it and/or modify it under the terms of the MIT License.

This software is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the MIT License for details.