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Previously collected language samples from 118 children between the ages of 3 and 11 years in age were manually and automatedly coded for DSS. The overall accuracy of DSSA was about 86%, while the mean point difference was approximately .7. DSSA generally scored language samples of children achieving lower manual DSS scores or children with language impairment with less accuracy than those of other children. While some precautions may need to be taken, accuracy levels are sufficiently high to allow the fully automated use of DSSA as an alternative to manual DSS scoring.
Developmental Sentence Scoring (DSS) is a standardized language sample analysis procedure that evaluates and scores a child’s use of standard American-English grammatical rules within complete sentences. Automated DSS programs have the potential to increase the efficiency and reduce the amount of time required for DSS analysis. The present study examines the accuracy of one automated DSS software program, DSSA 2.0, compared to manual DSS scoring on previously collected language samples from 30 children between the ages of 2; 5 and 7; 11. Additionally, this study seeks to determine the source of error in the automated score by comparing DSSA 2.0 analysis given manually versus automatedly assigned grammatical tag input. The overall accuracy of DSSA 2.0 was 86%; the accuracy of individual grammatical category-point value scores varied greatly. No statistically significant difference was found between the two DSSA 2.0 input conditions (manual vs. automated tags) suggesting that the underlying grammatical tagging is not the primary source of error in DSSA 2.0 analysis.
A variety of tools have been developed to assist in the quantification and analysis of naturalistic language samples. In recent years, computer technology has been employed in language sample analysis. This study compares a new automated index, Scoring Sentences Developmentally (SSD), to two existing measures. Eighty samples from three corpora were manually analyzed using DSS and MLU and the processed by the automated software. Results show all three indices to be highly correlated, with correlations ranging from .62 to .98. The high correlations among scores support further investigation of the psychometric characteristics of the SSD software to determine its clinical validity and reliability. Results of this study suggest that SSD has the potential to compliment other analysis procedures in assessing the language development of young children.
Spanning the entire childhood developmental period, Language Disorders from Infancy Through Adolescence, 5th Edition is the go-to text for learning how to properly assess childhood language disorders and provide appropriate treatment. The most comprehensive title available on childhood language disorders, it uses a descriptive-developmental approach to present basic concepts and vocabulary, an overview of key issues and controversies, the scope of communicative difficulties that make up child language disorders, and information on how language pathologists approach the assessment and intervention processes. This new edition also features significant updates in research, trends, social skills assessment, and instruction best practices. Clinical application focus featuring case studies, clinical vignettes, and suggested projects helps you apply concepts to professional practice. UNIQUE! Practice exercises with sample transcripts allow you to apply different methods of analysis. UNIQUE! Helpful study guides at the end of each chapter help you review and apply what you have learned. Highly regarded lead author who is an expert in language disorders in children provides authoritative guidance on the diagnosis and management of pediatric language disorders. More than 230 tables and boxes summarize important information such as dialogue examples, sample assessment plans, assessment and intervention principles, activities, and sample transcripts. Student/Professional Resources on Evolve include an image bank, video clips, and references linked to PubMed. NEW! Common core standards for language arts incorporated into the preschool and school-age chapters. NEW! Updated content features the latest research, theories, trends and techniques in the field. Information on preparing high-functioning students with autism for college Social skills training for students with autism The role of the speech-language pathologist on school literacy teams and in response to intervention Emerging theories of etiology and psychopathology added to Models of Child Language Disorders chapter Use of emerging technologies for assessment and intervention
Automated Speaking Assessment: Using Language Technologies to Score Spontaneous Speech provides a thorough overview of state-of-the-art automated speech scoring technology as it is currently used at Educational Testing Service (ETS). Its main focus is related to the automated scoring of spontaneous speech elicited by TOEFL iBT Speaking section items, but other applications of speech scoring, such as for more predictable spoken responses or responses provided in a dialogic setting, are also discussed. The book begins with an in-depth overview of the nascent field of automated speech scoring—its history, applications, and challenges—followed by a discussion of psychometric considerations for automated speech scoring. The second and third parts discuss the integral main components of an automated speech scoring system as well as the different types of automatically generated measures extracted by the system features related to evaluate the speaking construct of communicative competence as measured defined by the TOEFL iBT Speaking assessment. Finally, the last part of the book touches on more recent developments, such as providing more detailed feedback on test takers’ spoken responses using speech features and scoring of dialogic speech. It concludes with a discussion, summary, and outlook on future developments in this area. Written with minimal technical details for the benefit of non-experts, this book is an ideal resource for graduate students in courses on Language Testing and Assessment as well as teachers and researchers in applied linguistics.