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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.
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Quantifying the role of vocabulary knowledge in predicting future word learning
Can we predict the words a child is going to learn next given information about the words that a child knows now? Do different representations of a child’s vocabulary knowledge affect our ability to predict the acquisition of lexical items for individual children? Past research has often focused on population statistics of vocabulary growth rather than prediction of words an individual child is likely to learn next. We consider a neural network approach to predict vocabulary acquisition. Specifically, we investigate how best to represent the child’s current vocabulary in order to accurately predict future learning. The models we consider are based on qualitatively different sources of information: descriptive information about the child, the specific words a child knows, and representations that aim to capture the child’s aggregate lexical knowledge. Using longitudinal vocabulary data from children aged 15-36 months, we construct neural network models to predict which words are likely to be learned by a particular child in the coming month. Many models based on child-specific vocabulary information outperform models with child information only, suggesting that the words a child knows influence prediction of future language learning. These models provide an understanding of the role of current vocabulary knowledge on future lexical growth.
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- Award ID(s):
- 1631428
- PAR ID:
- 10113806
- Date Published:
- Journal Name:
- IEEE transactions on cognitive and developmental systems
- ISSN:
- 2379-8920
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TCDS.2019.2928023
