Inferring Concept Prerequisite Relations from Online Educational Resources

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

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.

Published
2019-07-17
Section
IAAI Technical Track: Emerging Papers