skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


This content will become publicly available on July 1, 2026

Title: Relative importance of early literacy and executive function skills on Spanish–English emergent bilinguals’ English reading achievement across primary years.
Award ID(s):
1749275
PAR ID:
10625437
Author(s) / Creator(s):
Publisher / Repository:
American Psychological Association
Date Published:
Journal Name:
Journal of Educational Psychology
Volume:
117
Issue:
5
ISSN:
0022-0663
Page Range / eLocation ID:
701 to 716
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. This corpus was collected in the Language Sciences Research Lab, a working lab embedded inside of a science museum: the Center of Science and Industry in Columbus, Ohio, USA. Participants were recruited from the floor of the museum and run in a semi-public space. Three distinctive features of the corpus are: (1) an interactive social robot (specifically, a Jibo robot) was present and participated in the sessions for roughly half the children; (2) all children were recorded with a lapel mic generating high quality audio (available through CHILDES), as well as a distal table mic generating low quality audio (available on request) to facilitate strong tests of automated speech processing on the data; and (3) the data were collected in the peri-pandemic period, beginning in the summer of 2021 just after COVID-19 restrictions were being eased and ending in the summer of 2022 – thus providing a snapshot of language development in a distinctive time of the world. A YouTube video on the Jibo robot is available here . 
    more » « less
  2. Lierler, Yuliya; Morales, Jose F; Dodaro, Carmine; Dahl, Veroniica; Gebser, Martin; Tekle, Tuncay (Ed.)
    Knowledge representation and reasoning (KRR) systems represent knowledge as collections of facts and rules. Like databases, KRR systems contain information about domains of human activities like industrial enterprises, science, and business. KRRs can represent complex concepts and relations, and they can query and manipulate information in sophisticated ways. Unfortunately, the KRR technology has been hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and professional knowledge engineers are hard to find. One solution could be to extract knowledge from English text, and a number of works have attempted to do so (OpenSesame, Google's Sling, etc.). Unfortunately, at present, extraction of logical facts from unrestricted natural language is still too inaccurate to be used for reasoning, while restricting the grammar of the language (so-called controlled natural language, or CNL) is hard for the users to learn and use. Nevertheless, some recent CNL-based approaches, such as the Knowledge Authoring Logic Machine (KALM), have shown to have very high accuracy compared to others, and a natural question is to what extent the CNL restrictions can be lifted. In this paper, we address this issue by transplanting the KALM framework to a neural natural language parser, mStanza. Here we limit our attention to authoring facts and queries and therefore our focus is what we call factual English statements. Authoring other types of knowledge, such as rules, will be considered in our followup work. As it turns out, neural network based parsers have problems of their own and the mistakes they make range from part-of-speech tagging to lemmatization to dependency errors. We present a number of techniques for combating these problems and test the new system, KALMFL (i.e., KALM for factual language), on a number of benchmarks, which show KALMFL achieves correctness in excess of 95%. 
    more » « less
  3. Purpose:This study examined the race identification of Southern American English speakers from two geographically distant regions in North Carolina. The purpose of this work is to explore how talkers' self-identified race, talker dialect region, and acoustic speech variables contribute to listener categorization of talker races. Method:Two groups of listeners heard a series of /h/–vowel–/d/ (/hVd/) words produced by Black and White talkers from East and West North Carolina, respectively. Results:Both Southern (North Carolina) and Midland (Indiana) listeners accurately categorized the race of all speakers with greater-than-chance accuracy; however, Western North Carolina Black talkers were categorized with the lowest accuracy, just above chance. Conclusions:The results suggest that similarities in the speech production patterns of West North Carolina Black and White talkers affect the racial categorization of Black, but not White talkers. The results are discussed with respect to the acoustic spectral features of the voices present in the sample population. 
    more » « less