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Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data

رجال البيئة غير المتجانسة الشبكات العصبية لمفهوم الشرط الأساسي التعلم في البيانات التعليمية

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 Publication date 2021
and research's language is English
 Created by Shamra Editor




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Prerequisite relations among concepts are crucial for educational applications, such as curriculum planning and intelligent tutoring. In this paper, we propose a novel concept prerequisite relation learning approach, named CPRL, which combines both concept representation learned from a heterogeneous graph and concept pairwise features. Furthermore, we extend CPRL under weakly supervised settings to make our method more practical, including learning prerequisite relations from learning object dependencies and generating training data with data programming. Our experiments on four datasets show that the proposed approach achieves the state-of-the-art results comparing with existing methods.



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