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Chen, Jing; Zhang, Mo; Bejar, Isaac I. – ETS Research Report Series, 2017
Automated essay scoring (AES) generally computes essay scores as a function of macrofeatures derived from a set of microfeatures extracted from the text using natural language processing (NLP). In the "e-rater"® automated scoring engine, developed at "Educational Testing Service" (ETS) for the automated scoring of essays, each…
Descriptors: Computer Assisted Testing, Scoring, Automation, Essay Tests
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Crossley, Scott; Clevinger, Amanda; Kim, YouJin – Language Assessment Quarterly, 2014
There has been a growing interest in the use of integrated tasks in the field of second language testing to enhance the authenticity of language tests. However, the role of text integration in test takers' performance has not been widely investigated. The purpose of the current study is to examine the effects of text-based relational (i.e.,…
Descriptors: Language Proficiency, Connected Discourse, Language Tests, English (Second Language)
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Xi, Xiaoming; Higgins, Derrick; Zechner, Klaus; Williamson, David – Language Testing, 2012
This paper compares two alternative scoring methods--multiple regression and classification trees--for an automated speech scoring system used in a practice environment. The two methods were evaluated on two criteria: construct representation and empirical performance in predicting human scores. The empirical performance of the two scoring models…
Descriptors: Scoring, Classification, Weighted Scores, Comparative Analysis
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Wellman, Gregory S. – Journal of Interactive Online Learning, 2005
The purpose of this study was to examine the impact that proctored versus un-proctored testing would have on learning for an on-line content module; and examine the relationship between LASSI variables and learning. A randomized, pre-test/post-test control group design was employed. College students in a pharmacy curriculum, were randomized to two…
Descriptors: Comparative Analysis, Cognitive Style, Measures (Individuals), Online Courses