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TrueLearn

The code that allows you to run truelearn experiments that tests the performance of TrueLearn: A bayesian learning strategy that models lifelong learner engagement incorperating background knowledge of the learner and novelty of educational resources.

Citing the paper

This work is related to the paper TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources published at the Thirty-forth AAAI Conference on Artifical Intelligence, 2020 at New York, NY, USA.

The bibtex entry for the publication is as follows:

@inproceedings{truelearn2020,
	author = {Bulathwela, S. and Perez-Ortiz, M. and Yilmaz, E. and Shawe-Taylor, J.},
	title={TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources},
	booktitle = {AAAI Conference on Artificial Intelligence},
	year = {2020}
}

About the files

All the useful files in this module incorporate different algorithms used in the experiments

  • naive_baseline_models.py: file contains the programming logic of persistence, majority models
  • multi_skill_kt_models.py: file contains the programming logic of multi-skill knowledge tracing models
  • fixed_depth_truelearn_models.py: file contains the programming logic of fixed-depth truelearn models
  • truelearn_models.py: file contains the programming logic of TrueLearn Novel model

Additionally, there are two files that are used to run the experiment:

  • run_sequencial_trueskill_baseline.py: file that is used to run Vanilla TrueSkill baseline and TrueLearn dynamic-depth as they are sequential models
  • run_experiments.py: file that is used to run all the other experiments

Other helper files:

  • utils.py: file contains required utility functions
  • analyse_results.py: file used to produce precision, recall, accuracy and F1 metrics from the algorithms

About

This repository contains all the code and instructions on setting up the environment and running the TrueLearn algorithm

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