{"type":"ScholarlyArticle","title":"Improving Knowledge Tracing via Pre-training Question Embeddings","authors":[{"name":"Yunfei Liu","slug":"yunfei-liu","given":"Yunfei","family":"Liu","affiliation":"Shanghai Jiao Tong University"},{"name":"Yang Yang","slug":"yang-yang","given":"Yang","family":"Yang","affiliation":"Shanghai Jiao Tong University"},{"name":"Xianyu Chen","slug":"xianyu-chen","given":"Xianyu","family":"Chen","affiliation":"Shanghai Jiao Tong University"},{"name":"Jian Shen","slug":"jian-shen","given":"Jian","family":"Shen","affiliation":"Shanghai Jiao Tong University"},{"name":"Haifeng Zhang","slug":"haifeng-zhang","given":"Haifeng","family":"Zhang","affiliation":"The Center on Frontiers of Computing Studies , Peking University"},{"name":"Yong Yu","slug":"yong-yu","given":"Yong","family":"Yu","affiliation":"Shanghai Jiao Tong University"}],"abstract":"Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response. Although much research has been devoted to exploiting the question information, plentiful advanced information among questions and skills hasn't been well extracted, making it challenging for previous work to perform adequately. In this paper, we demonstrate that large gains on KT can be realized by pre-training embeddings for each question on abundant side information, followed by training deep KT models on the obtained embeddings. To be specific, the side information includes question difficulty and three kinds of relations contained in a bipartite graph between questions and skills. To pre-train the question embeddings, we propose to use product-based neural networks to recover the side information. As a result, adopting the pre-trained embeddings in existing deep KT models significantly outperforms state-of-the-art baselines on three common KT datasets.","doi":"10.24963/ijcai.2020/219","url":"https://ijcai.org/proceedings/2020/219","pdf":"https://ijcai.org/proceedings/2020/0219.pdf","markdown":"https://ijcai.org/proceedings/2020/219.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/219","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Humans and AI","track":"Main track","number":219,"pages":{"first":1577,"last":1583},"keywords":["Humans and AI: Cognitive Modeling","Humans and AI: Computer-Aided Education"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24548/file_24548.m3u8","length_sec":292,"provider":"hls"}],"references":[{"position":1,"raw":"[Abdelrahman and Wang, 2019] Ghodai Abdelrahman and Qing Wang. 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