Inferring Concept Prerequisite Relations from Online Educational Resources

Authors

  • Sudeshna Roy Videoken
  • Meghana Madhyastha International Institute of Information Technology Bangalore
  • Sheril Lawrence International Institute of Information Technology Bangalore
  • Vaibhav Rajan National University of Singapore

DOI:

https://doi.org/10.1609/aaai.v33i01.33019589

Abstract

The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course prerequisites.

Downloads

Published

2019-07-17

How to Cite

Roy, S., Madhyastha, M., Lawrence, S., & Rajan, V. (2019). Inferring Concept Prerequisite Relations from Online Educational Resources. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9589-9594. https://doi.org/10.1609/aaai.v33i01.33019589

Issue

Section

IAAI Technical Track: Emerging Papers