Automated Essay Scoring : A Cross-disciplinary Perspective

By M. Shermis, Jill Burstein. 2003

705 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/88e7960b69752fb5876060d029a0024dfe6b6345

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Citations in the corpus

COGNITIVE MODELS OF WRITING: WRITING PROFICIENCY AS A COMPLEX INTEGRATED SKILL (2008, methodology): “Given ETS’s existing automated essay-scoring technology (Burstein & Shermis, 2003) and other existing technologies for automated essay scoring (Shermis & Burstein, 2003), there is already a strong foundation for applying automated scoring in the CBAL context.”
Automatic Text Scoring Using Neural Networks (2016, background): “…with regards to ATS systems of text produced by non-native English-language learners (Page, 1968; Attali and Burstein, 2006; Rudner and Liang, 2002; Elliot, 2003; Landauer et al., 2003; Briscoe et al., 2010; Yannakoudakis et al., 2011; Sakaguchi et al., 2015, among others), overviews of which can…”
Differences in written discourse in independent and integrated prototype tasks for next generation TOEFL (2005, methodology): “these categories were adapted from research on reported speech by Thompson (1996) and Plungian, (2001) and on summary writing by Day (1986), Kintsch (1990), and Winograd (1984).”
Automated writing evaluation: defining the classroom research agenda (2006, background): “AWE systems have been under development since the 1960s, when a national network of US universities, known as the College Board, supported the development of Project Essay Grade to help score thousands of high school student essays ( Page, 2003 ).”
Competency assessment models. (2007, methodology): “In very high-volume situations, software designed to assist in automating the scoring of essays and short-answer questions is available (Shermis & Burstein, 2003).”
A New Dataset and Method for Automatically Grading ESOL Texts (2011, methodology): “Finally, using a set of ‘outlier’ texts, we test the validity of our model and identify cases where the model’s scores diverge from that of a human examiner.”
AUTOMATED ESSAY SCORING WITH E‐RATER® V.2.0 (2004, methodology): “Content vector analysis is applied in the following manner in e-rater: first each essay and, in addition, a set of training essays from each score point, is converted to vectors whose elements are weights for each word in the individual essay or in the set of training essays for each score point…”
An Overview of Automated Scoring of Essays. (2006, background): “Despite its weaknesses, AES continues attracting the attention of public schools, universities, testing companies, researchers and educators (Burstein, Kukich, Wolff, Lu, & Chodorow, 1998; Shermis & Burstein, 2003; Sireci & Rizavi, 1999).”
The Eras and Trends of Automatic Short Answer Grading (2015, background): “Here, ASAG systems tend to focus more on content, whilst automatic essay grading (AEG) systems (Shermis and Burstein 2003, 2013) tend to focus more on style (Gütl 2007; Pérez-Marı́n 2004).”
Training Advanced Writing Skills: The Case for Deliberate Practice (2009, background): “Shermis and Burstein (2003) reviewed several computer-based scoring and feedback methods derived from cognitive psychology and computational linguistics.”