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- W2955369613 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." @default.
- W2955369613 created "2019-07-12" @default.
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- W2955369613 date "2018-11-01" @default.
- W2955369613 modified "2023-09-23" @default.
- W2955369613 title "Inferring Concept Prerequisite Relations from Online Educational Resources" @default.
- W2955369613 hasPublicationYear "2018" @default.
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