{"type":"ScholarlyArticle","title":"A Semantics-based Model for Predicting Children's Vocabulary","authors":[{"name":"Ishaan Grover","slug":"ishaan-grover","given":"Ishaan","family":"Grover","affiliation":"Massachusetts Institute of Technology 77 Massachusetts Avenue , Cambridge , Massachusetts , 02139"},{"name":"Hae Won Park","slug":"hae-won-park","given":"Hae Won","family":"Park","affiliation":"Massachusetts Institute of Technology 77 Massachusetts Avenue , Cambridge , Massachusetts , 02139"},{"name":"Cynthia Breazeal","slug":"cynthia-breazeal","given":"Cynthia","family":"Breazeal","affiliation":"Massachusetts Institute of Technology 77 Massachusetts Avenue , Cambridge , Massachusetts , 02139"}],"abstract":"Intelligent tutoring systems (ITS) provide educational benefits through one-on-one tutoring by assessing children's existing knowledge and providing tailored educational content. In the domain of language acquisition, several studies have shown that children often learn new words by forming semantic relationships with words they already know. In this paper, we present a model that uses word semantics (semantics-based model) to make inferences about a child's vocabulary from partial information about their existing vocabulary knowledge. We show that the proposed semantics-based model outperforms models that do not use word semantics (semantics-free models) on average. A subject-level analysis of results reveals that different models perform well for different children, thus motivating the need to combine predictions. To this end, we use two methods to combine predictions from semantics-based and semantics-free models and show that these methods yield better predictions of a child's vocabulary knowledge. Our results motivate the use of semantics-based models to assess children's vocabulary knowledge and build ITS that maximizes children's semantic understanding of words.","doi":"10.24963/ijcai.2019/188","url":"https://ijcai.org/proceedings/2019/188","pdf":"https://ijcai.org/proceedings/2019/0188.pdf","markdown":"https://ijcai.org/proceedings/2019/188.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/188","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Humans and AI","track":"Main track","number":188,"pages":{"first":1358,"last":1365},"keywords":["Knowledge Representation and Reasoning: Reasoning about Knowlege and Belief","Uncertainty in AI: Graphical Models","Humans and AI: Cognitive Modeling","Machine Learning Applications: Other Applications","Humans and AI: Computer-Aided Education","Uncertainty in AI: Approximate Probabilistic Inference"],"videos":[],"references":[{"position":1,"raw":"[Bailer-Jones and Smith, 2011] C Bailer-Jones and K Smith. 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