Novel Approaches to Language Assessment Part 2: Evaluating Measures of Effort in a Sentence Recall Task for School-Age Children

PURPOSE: Children with developmental language disorder (DLD) produce more speech disruptions and have a slower speech rate than their peers. This study validated measures of effortful sentence production by examining their association with general language ability and differences between groups of children with DLD and typical language development (TLD). Measures of effort were expected to be related to general language ability, and children with DLD were expected to produce more stall-type disruptions (i.e., repetitions, filled pauses, and unfilled pauses) and use fewer words per minute (WPM) than their peers with TLD. METHOD: = 90.64 months) were transcribed, aligned to the audio, and coded for sentence disruptions using the Codes for the Human Analysis of Transcripts form from TalkBank. A custom-made Python script computed measures of stall rate, nondisrupted rate (e.g., the percentage of utterances with no disruptions), and WPM and were examined relative to child performance on the Clinical Evaluation of Language Fundamentals-Fourth Edition. RESULTS: When controlling for age, stall rate and nondisrupted rate were moderately correlated with general language ability, and WPM was strongly correlated with general language ability. The DLD group used significantly more stalls and fewer WPM and nondisrupted utterances than did the TLD group. CONCLUSIONS: The findings demonstrate that measures of effortful sentence production are systematically associated with language ability. Future directions for leveraging artificial intelligence to automate these measures and exploring their utility as value-added indicators of DLD are discussed.

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Publication Details

Journal
Language Speech and Hearing Services in Schools
Published
2026-10-09
DOI
https://doi.org/10.1044/2026_lshss-25-00256
Primary Topic
Language Development and Disorders
Type
article
Field-Weighted Citation Impact
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article

Novel Approaches to Language Assessment Part 2: Evaluating Measures of Effort in a Sentence Recall Task for School-Age Children

Dancheng Liu, Jinjun Xiong, Pamela A. Hadley, Carol Miller et al.
Language Speech and Hearing Services in Schools
Language Development and Disorders
article

Novel Approaches to Language Assessment Part 2: Evaluating Measures of Effort in a Sentence Recall Task for School-Age Children

Dancheng Liu, Jinjun Xiong, Pamela A. Hadley, Carol Miller, Sean M. Redmond, Tracy Preza, Abdlrahman Alabdallah
article en

Abstract

PURPOSE: Children with developmental language disorder (DLD) produce more speech disruptions and have a slower speech rate than their peers. This study validated measures of effortful sentence production by examining their association with general language ability and differences between groups of children with DLD and typical language development (TLD). Measures of effort were expected to be related to general language ability, and children with DLD were expected to produce more stall-type disruptions (i.e., repetitions, filled pauses, and unfilled pauses) and use fewer words per minute (WPM) than their peers with TLD. METHOD: = 90.64 months) were transcribed, aligned to the audio, and coded for sentence disruptions using the Codes for the Human Analysis of Transcripts form from TalkBank. A custom-made Python script computed measures of stall rate, nondisrupted rate (e.g., the percentage of utterances with no disruptions), and WPM and were examined relative to child performance on the Clinical Evaluation of Language Fundamentals-Fourth Edition. RESULTS: When controlling for age, stall rate and nondisrupted rate were moderately correlated with general language ability, and WPM was strongly correlated with general language ability. The DLD group used significantly more stalls and fewer WPM and nondisrupted utterances than did the TLD group. CONCLUSIONS: The findings demonstrate that measures of effortful sentence production are systematically associated with language ability. Future directions for leveraging artificial intelligence to automate these measures and exploring their utility as value-added indicators of DLD are discussed.

Language Speech and Hearing Services in Schools
Pennsylvania State University (US), University of Illinois Urbana-Champaign (US), University of Utah (US), University at Buffalo, State University of New York (US)
Openalex Percentile: Top 5%
Language Development and Disorders
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