Combination of Multiple Regressoion and Text Categorization in Automated Essay Scoring of College English Writing

2013 ◽  
Vol 12 (24) ◽  
pp. 7977-7982
Author(s):  
Shili Ge
2013 ◽  
Vol 274 ◽  
pp. 654-657
Author(s):  
Xue Mei Yao

Since its invention in the 1990s, an immense body of academic literature on LSA regarding to Automated Essay Scoring (AES) has been published. The current study investigated the extent of applicability and usefulness of LSA-based AES for assigning English essays written by Chinese college students. The statistical results showed the application of LSA-based AES in Chinese EFL context is to the limited context. The reason is that LSA-based AES focuses on content while Chinese English writing test are grammar-oriented.


PsycCRITIQUES ◽  
2004 ◽  
Vol 49 (Supplement 14) ◽  
Author(s):  
Steven E. Stemler

2009 ◽  
Author(s):  
Ronald T. Kellogg ◽  
Alison P. Whiteford ◽  
Thomas Quinlan

2019 ◽  
Vol 113 (1) ◽  
pp. 9-30
Author(s):  
Kateřina Rysová ◽  
Magdaléna Rysová ◽  
Michal Novák ◽  
Jiří Mírovský ◽  
Eva Hajičová

Abstract In the paper, we present EVALD applications (Evaluator of Discourse) for automated essay scoring. EVALD is the first tool of this type for Czech. It evaluates texts written by both native and non-native speakers of Czech. We describe first the history and the present in the automatic essay scoring, which is illustrated by examples of systems for other languages, mainly for English. Then we focus on the methodology of creating the EVALD applications and describe datasets used for testing as well as supervised training that EVALD builds on. Furthermore, we analyze in detail a sample of newly acquired language data – texts written by non-native speakers reaching the threshold level of the Czech language acquisition required e.g. for the permanent residence in the Czech Republic – and we focus on linguistic differences between the available text levels. We present the feature set used by EVALD and – based on the analysis – we extend it with new spelling features. Finally, we evaluate the overall performance of various variants of EVALD and provide the analysis of collected results.


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