Inferring Concept Prerequisite Relations from Online Educational Resources
This addresses the need for automated curriculum creation and personalized recommendations in online education, representing an incremental improvement over existing methods.
The authors tackled the problem of inferring prerequisite relations between educational concepts from online resources, presenting PREREQ, a supervised learning method that outperforms state-of-the-art approaches on benchmark datasets and can learn effectively from minimal training data.
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.