Fostering parent-child dialog through automated discussion suggestions

The development of early literacy skills has been critically linked to a child's later academic success. In particular, repeated studies have shown that reading aloud to children and providing opportunities for them to discuss the stories that they hear is of utmost importance to later academic...

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Bibliographic Details
Main Authors: Boteanu, Adrian (Author), Chernova, Sonia (Author), Nunez, David Scott (Contributor), Breazeal, Cynthia Lynn (Author)
Other Authors: Massachusetts Institute of Technology. Media Laboratory (Contributor), Program in Media Arts and Sciences (Massachusetts Institute of Technology) (Contributor), Breazeal, Cynthia L. (Contributor)
Format: Article
Language:English
Published: Springer Netherlands, 2017-03-03T23:57:17Z.
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Summary:The development of early literacy skills has been critically linked to a child's later academic success. In particular, repeated studies have shown that reading aloud to children and providing opportunities for them to discuss the stories that they hear is of utmost importance to later academic success. CloudPrimer is a tablet-based interactive reading primer that aims to foster early literacy skills by supporting parents in shared reading with their children through user-targeted discussion topic suggestions. The tablet application records discussions between parents and children as they read a story and, in combination with a common sense knowledge base, leverages this information to produce suggestions. Because of the unique challenges presented by our application, the suggestion generation method relies on a novel topic modeling method that is based on semantic graph topology. We conducted a user study in which we compared how delivering suggestions generated by our approach compares to expert-crafted suggestions. Our results show that our system can successfully improve engagement and parent-child reading practices in the absence of a literacy expert's tutoring.
National Science Foundation (U.S.) (Award Number 1117584)