Document Type
Article
Publication Date
February 2018
Publication Title
Language Learning & Technology
Volume
22
Issue Number
1
First Page
137
Last Page
156
DOI
10125/44585
Keywords
Assessment, Testing
Disciplines
Artificial Intelligence and Robotics | Computational Linguistics | Computer Sciences | Educational Assessment, Evaluation, and Research | First and Second Language Acquisition | Spanish Linguistics
Abstract
In response to the need for examples of test validation from which everyday language programs can benefit, this paper reports on a study that used Bachman’s (2005) assessment use argument (AUA) framework to examine evidence to support claims made about the intended interpretations and uses of scores based on a new web-based Spanish language placement test. The test, which consisted of 100 items distributed across five item types (sound discrimination, grammar, listening comprehension, reading comprehension, and vocabulary), was tested with 2,201 incoming first-year and transfer students at a large, Midwestern public university. Analyses of internal consistency and validity revealed the test to be reliable and valid with regard to its functionality, the content covered on the exam, and the consistency with which placement decisions could be made. Findings are discussed in light of the AUA model developed for the placement test, and practical suggestions for university-level language program instructors and testing administrators are outlined.
Recommended Citation
Avizia Long, Sun-Young Shin, Kimberly Geeslin, and Erik Willis. "Does the test work? Evaluating a web-based language placement test" Language Learning & Technology (2018): 137-156. https://doi.org/10125/44585
Included in
Artificial Intelligence and Robotics Commons, Computational Linguistics Commons, Educational Assessment, Evaluation, and Research Commons, First and Second Language Acquisition Commons, Spanish Linguistics Commons
Comments
This article was published in Language Learning & Technology, volume 22, issue 1, 2018, and can also be found online at this link: https://doi.org/10125/44585
Copyright © 2018 Avizia Y. Long, Sun-Young Shin, Kimberly Geeslin, & Erik W. Willis.
LLT authors hold the copyright for articles published in the journal. Permission for republication should be obtained directly from the individual author(s). Please note that no derivatives of LLT articles and no commercial use of LLT articles are allowed. LLT must be acknowledged as the original source and appropriately cited.