A Semantics-based Model for Predicting Children's Vocabulary
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. 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 ac...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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International Joint Conferences on Artificial Intelligence
2021
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| Online Access: | https://hdl.handle.net/1721.1/137139 |
| _version_ | 1811069961865527296 |
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| author | Grover, Ishaan Park, Hae Won Breazeal, Cynthia |
| author_facet | Grover, Ishaan Park, Hae Won Breazeal, Cynthia |
| author_sort | Grover, Ishaan |
| collection | MIT |
| description | © 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. 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. |
| first_indexed | 2024-09-23T08:19:39Z |
| format | Article |
| id | mit-1721.1/137139 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:19:39Z |
| publishDate | 2021 |
| publisher | International Joint Conferences on Artificial Intelligence |
| record_format | dspace |
| spelling | mit-1721.1/1371392021-11-03T03:42:00Z A Semantics-based Model for Predicting Children's Vocabulary Grover, Ishaan Park, Hae Won Breazeal, Cynthia © 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. 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. 2021-11-02T17:38:32Z 2021-11-02T17:38:32Z 2019 2021-06-24T14:52:20Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137139 Grover, Ishaan, Park, Hae Won and Breazeal, Cynthia. 2019. "A Semantics-based Model for Predicting Children's Vocabulary." IJCAI International Joint Conference on Artificial Intelligence, 2019-August. en 10.24963/IJCAI.2019/188 IJCAI International Joint Conference on Artificial Intelligence Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf International Joint Conferences on Artificial Intelligence Other repository |
| spellingShingle | Grover, Ishaan Park, Hae Won Breazeal, Cynthia A Semantics-based Model for Predicting Children's Vocabulary |
| title | A Semantics-based Model for Predicting Children's Vocabulary |
| title_full | A Semantics-based Model for Predicting Children's Vocabulary |
| title_fullStr | A Semantics-based Model for Predicting Children's Vocabulary |
| title_full_unstemmed | A Semantics-based Model for Predicting Children's Vocabulary |
| title_short | A Semantics-based Model for Predicting Children's Vocabulary |
| title_sort | semantics based model for predicting children s vocabulary |
| url | https://hdl.handle.net/1721.1/137139 |
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