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690 resources
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Ou Lydia Liu, Chris Brew, John Blackmore...|Mar 6th, 2014|journalArticleOu Lydia Liu, Chris Brew, John Blackmore...Mar 6th, 2014
Content‐based automated scoring has been applied in a variety of science domains. However, many prior applications involved simplified scoring rubrics without considering rubrics representing multiple levels of understanding. This study tested a concept‐based scoring tool for content‐based scoring, c‐rater™, for four science items with rubrics aiming to differentiate among multiple levels of understanding. The items showed moderate to good agreement with human scores. The findings suggest...
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Russell G. Almond|Dec 11th, 2013|journalArticleRussell G. AlmondDec 11th, 2013
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J.A. León, R. Olmos, I. Escudero|Jul 1st, 2013|journalArticleJ.A. León, R. Olmos, I. EscuderoJul 1st, 2013
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Questar Assessment, Inc.|Apr 1st, 2013|reportQuestar Assessment, Inc.Apr 1st, 2013
A reverse engineering approach to automatic item generation (AIG) was applied to a figurebased publicly released test item from the Organisation for Economic Cooperation and Development (OECD) Programme for International Student Assessment (PISA) mathematical literacy cognitive instrument as part of a proof of concept. The author created an item template from which three items were randomly generated from within each of six types defined by a feature deemed to be most likely to affect item...
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Sara Cushing Weigle|Jan 1st, 2013|journalArticleSara Cushing WeigleJan 1st, 2013
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Sara Cushing Weigle|Jan 1st, 2013|journalArticleSara Cushing WeigleJan 1st, 2013
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Mark J. Gierl, Thomas M. Haladyna|Dec 1st, 2013|bookMark J. Gierl, Thomas M. HaladynaDec 1st, 2013
"Automatic item generation (AIG) represents a relatively new and unique research area where specific cognitive and psychometric theories are applied to test construction practices for the purpose of producing test items using technology. The purpose of this book is to bring researchers and practitioners up-to-date on the growing body of research on AIG by organizing in one volume what is currently known about this research area. Part I begins with an overview of the concepts and topics...
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Kellie Morrissey, Jurek Kirakowski, Masa...|Dec 1st, 2013|bookSectionKellie Morrissey, Jurek Kirakowski, Masa...Dec 1st, 2013
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Yoav Cohen, Yael Safran|Apr 1st, 2012|conferencePaperYoav Cohen, Yael SafranApr 1st, 2012
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Brent Bridgeman, Catherine Trapani, Yiga...|Jan 1st, 2012|journalArticleBrent Bridgeman, Catherine Trapani, Yiga...Jan 1st, 2012
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Issac I. Bejar|Sep 1st, 2012|journalArticleIssac I. BejarSep 1st, 2012
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Brent Bridgeman, Catherine Trapani, Yiga...|Jan 1st, 2012|journalArticleBrent Bridgeman, Catherine Trapani, Yiga...Jan 1st, 2012
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Lise Getoor, Ashwin Machanavajjhala|Aug 1st, 2012|journalArticleLise Getoor, Ashwin MachanavajjhalaAug 1st, 2012
This tutorial brings together perspectives on ER from a variety of fields, including databases, machine learning, natural language processing and information retrieval, to provide, in one setting, a survey of a large body of work. We discuss both the practical aspects and theoretical underpinnings of ER. We describe existing solutions, current challenges, and open research problems.
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David M. Williamson, Xiaoming Xi, F. Jay...|Mar 1st, 2012|journalArticleDavid M. Williamson, Xiaoming Xi, F. Jay...Mar 1st, 2012
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Miao Chen, Klaus Zechner|Jun 1st, 2011|conferencePaperMiao Chen, Klaus ZechnerJun 1st, 2011
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Cynthia Dwork, Moritz Hardt, Toniann Pit...|Dec 1st, 2011|journalArticleCynthia Dwork, Moritz Hardt, Toniann Pit...Dec 1st, 2011
We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in some group, while maintaining utility for the classifier (the university). The main conceptual contribution of this paper is a framework for fair classification comprising (1) a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the...
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T. Solorio, M. Sherman, Y. Liu|Oct 22nd, 2010|journalArticleT. Solorio, M. Sherman, Y. LiuOct 22nd, 2010
In this work we study how features typically used in natural language processing tasks, together with measures from syntactic complexity, can be adapted to the problem of developing language profiles of bilingual children. Our experiments show that these features can provide high discriminative value for predicting language dominance from story retells in a Spanish–English bilingual population of children. Moreover, some of our proposed features are even more powerful than measures commonly...
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R.S.J.d Baker, B. McGaw, P. Peterson|Dec 1st, 2010|bookSectionR.S.J.d Baker, B. McGaw, P. PetersonDec 1st, 2010
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Xiaoming Xi|Jul 1st, 2010|journalArticleXiaoming XiJul 1st, 2010
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Ehud Reiter, Anja Belz|Dec 1st, 2009|journalArticleEhud Reiter, Anja BelzDec 1st, 2009
There is growing interest in using automatically computed corpus-based evaluation metrics to evaluate Natural Language Generation (NLG) systems, because these are often considerably cheaper than the human-based evaluations which have traditionally been used in NLG. We review previous work on NLG evaluation and on validation of automatic metrics in NLP, and then present the results of two studies of how well some metrics which are popular in other areas of NLP (notably BLEU and ROUGE)...