The Child Language Data Exchange System in research on second language acquisition

2001 ◽  
Vol 17 (2) ◽  
pp. 195-212
Author(s):  
William Rutherford ◽  
Margaret Thomas
Keyword(s):  
Second Language ◽  
Second Language Acquisition ◽  
Language Acquisition ◽  
Data Exchange ◽  
Child Language ◽  
Exchange System ◽  
Language Data

Machine-Scored Syntax: Comparison of the CLAN Automatic Scoring Program to Manual Scoring

2020 ◽  
Vol 51 (2) ◽  
pp. 479-493
Author(s):  
Jenny A. Roberts ◽  
Evelyn P. Altenberg ◽  
Madison Hunter
Keyword(s):  
Data Exchange ◽  
Child Language ◽  
Exchange System ◽  
Absolute Point ◽  
Language Analysis ◽  
Search Patterns ◽  
Language Data ◽  
Report Accuracy ◽  
Point To Point ◽  
Automatic Scoring

Purpose The results of automatic machine scoring of the Index of Productive Syntax from the Computerized Language ANalysis (CLAN) tools of the Child Language Data Exchange System of TalkBank (MacWhinney, 2000) were compared to manual scoring to determine the accuracy of the machine-scored method. Method Twenty transcripts of 10 children from archival data of the Weismer Corpus from the Child Language Data Exchange System at 30 and 42 months were examined. Measures of absolute point difference and point-to-point accuracy were compared, as well as points erroneously given and missed. Two new measures for evaluating automatic scoring of the Index of Productive Syntax were introduced: Machine Item Accuracy (MIA) and Cascade Failure Rate— these measures further analyze points erroneously given and missed. Differences in total scores, subscale scores, and individual structures were also reported. Results Mean absolute point difference between machine and hand scoring was 3.65, point-to-point agreement was 72.6%, and MIA was 74.9%. There were large differences in subscales, with Noun Phrase and Verb Phrase subscales generally providing greater accuracy and agreement than Question/Negation and Sentence Structures subscales. There were significantly more erroneous than missed items in machine scoring, attributed to problems of mistagging of elements, imprecise search patterns, and other errors. Cascade failure resulted in an average of 4.65 points lost per transcript. Conclusions The CLAN program showed relatively inaccurate outcomes in comparison to manual scoring on both traditional and new measures of accuracy. Recommendations for improvement of the program include accounting for second exemplar violations and applying cascaded credit, among other suggestions. It was proposed that research on machine-scored syntax routinely report accuracy measures detailing erroneous and missed scores, including MIA, so that researchers and clinicians are aware of the limitations of a machine-scoring program. Supplemental Material https://doi.org/10.23641/asha.11984364

The CHILDES Project: Tools for Analyzing Talk: Vol. 1. Transcription format and programs; Vol. 2. The database (3rd ed.). B. MacWhinney. Mahwah, NJ: Erlbaum, 2000. Pp. 366 (Vol. 1); Pp. 418 (Vol. 2).

2002 ◽  
Vol 23 (2) ◽  
pp. 304-306
Author(s):  
Diane E. Beals
Keyword(s):  
Data Sharing ◽  
Data Exchange ◽  
State Of The Art ◽  
Child Language ◽  
The State ◽  
Exchange System ◽  
Language Analysis ◽  
Language Data ◽  
User Friendly

Since the late 1980s, the Child Language Data Exchange System (CHILDES) has defined the state of the art of collection, analysis, archiving, and data sharing of transcriptions of children's language. Starting from scratch in 1987, Brian MacWhinney, along with many other leaders in child language, developed highly useful tools for the computerization of transcripts and their analysis. I have used the transcription conventions and analysis programs since 1989 and have seen the system evolve from a simple DOS-based program to one that handles much broader and more complex analyses within more user-friendly Windows and Macintosh platforms. This latest (third) edition of the manual that accompanies the CHILDES system reflects a more stable version of the Conventions for Human Analysis of Transcripts (CHAT) and Child Language Analysis (CLAN) programs than prior editions, which felt like works in progress. This version is written as a finished product with procedures and programs that have settled down into stable patterns of operation.

Indexen Voor Beurtwisselingsinitiatief in Tweede-Taal Verwerving

1996 ◽  
Vol 54 ◽  
pp. 103-115
Author(s):  
Resi Damhuis
Keyword(s):  
Second Language ◽  
Second Language Acquisition ◽  
Language Acquisition ◽  
Communicative Competence ◽  
Classroom Interactions ◽  
Turn Taking ◽  
Language Data ◽  
The Mean ◽  
Children's Development ◽  
Incomplete Picture

For optimal second language acquisition (SLA) classroom interactions have to allow normative speaking children to participate actively, because such initiative-rich participation enhances the children's development of linguistic and communicative competence. This article focuses on turn-taking initiatives. Turn-taking initiatives vary in their initiative strength. Restricting analysis exclusively to strong initiatives yields an incomplete picture of the contribution of a conversation to SLA. Less strong initiatives, too, must be taken into account. To analyze each utterance for its initiative value, a turn-taking taxonomy was developed, leading to turn-taking indices that represent the mean initiative value of all utterances by a speaker. Language data from kindergarten conversations show that only results on strong initiatives differ from results on turn-taking indices. It is argued that in order to assess the value of a conversation for SLA both kinds of measures, strong initiatives as well as indices, are necessary.

The child language data exchange system

1985 ◽  
Vol 12 (2) ◽  
pp. 271-295 ◽  
Author(s):  
Brian MacWhinney ◽  
Catherine Snow
Keyword(s):  
Data Exchange ◽  
Child Language ◽  
New Technology ◽  
Exchange System ◽  
Language Data ◽  
Share Data ◽  
Recorded Data ◽  
Family Setting ◽  
Dissemination Of Technology

ABSTRACTThe study of language acquisition underwent a major revolution in the late 1950s as a result of the dissemination of technology permitting high-quality tape-recording of children in the family setting. This new technology led to major breakthroughs in the quality of both data and theory. The field is now at the threshold of a possible second major breakthrough stimulated by the dissemination of personal computing. Researchers are now able to transcribe tape-recorded data into computer files. With this new medium it is easy to conduct global searches for word combinations across collections of files. It is also possible to enter new codings of the basic text line. Because of the speed and accuracy with which computer files can be copied, it is now much easier to share data between researchers. To foster this sharing of computerized data, a group of child language researchers has established the Child Language Data Exchange System (CHILDES). This article details the formation of the CHILDES, the governance of the system, the nature of the database, the shape of the coding conventions, and the types of computer programs being developed.

Computational Tools for Analysing Talk

1990 ◽  
Vol 13 (2) ◽  
pp. 187-199
Author(s):  
Kim Plunkett
Keyword(s):  
Data Exchange ◽  
Child Language ◽  
Research Community ◽  
Exchange System ◽  
Computational Tools ◽  
Future Developments ◽  
Software Packages ◽  
Wide Range ◽  
The World ◽  
Language Data

The Child Language Data Exchange System — CHILDES — is the largest child language archive in the world. The archive includes a wide range of languages covering both normal and abnormal populations. The database is freely accessible to the research community and the user is supported with guidelines for carrying out transcription work and software packages for the automatic analysis of transcriptions. The article provides a brief overview of the CHAT transcription notation and the CLAN programs that can be used to analyse transcripts written in CHAT format. Current drawbacks of the CHILDES system are discussed and some pointers to future developments higlighted.

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