Implementation and Applications of the Intelligent Essay Assessor

Foltz, Peter W.; Streeter, Lynn A.; Lochbaum, Karen E.; Landauer, Thomas K.

doi:10.4324/9780203122761.ch5

Cited by 53 publications

(61 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Baseline2 is LSA [9] trained on prescored essays and the text. While our first baseline came from the holistic scoring literature, LSA has been successfully used in trait-based systems to score content and ideas [8,12], which seems more similar to our task of scoring Evidence. Since we do not have a separate pre-scored set of training essays, we do cross-validation in our experiments.…”

Section: Methodsmentioning

confidence: 99%

“…Holistic scoring methods assess the overall quality of an essay by considering multiple criteria simultaneously in order to assign a single score. In contrast, trait-based scoring methods [10,8] can provide multiple scores, as they separately consider component parts or writing purposes when scoring an essay. While holistic methods are typically more efficient and provide more reliable scores, trait-based methods are better at providing diagnostic insight on student performance [16,2].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Scoring of an Analytical Response-To-Text Assessment

Rahimi

Litman

Correnti

et al. 2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In analytical writing in response to text, students read a complex text and adopt an analytic stance in their writing about it. To evaluate this type of writing at scale, an automated approach for Response to Text Assessment (RTA) is needed. With the long-term goal of producing informative feedback for students and teachers, we design a new set of interpretable features that operationalize the Evidence rubric of RTA. When evaluated on a corpus of essays written by students in grades 4-6, our results show that our features outperform baselines based on well-performing features from other types of essay assessments.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automatic Scoring of an Analytical Response-To-Text Assessment

Rahimi

Litman

Correnti

et al. 2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…The quality of content is often addressed by features that quantify the similarity between the vocabulary used in an essay and reference essays from given score points (Attali and Burstein, 2006;Foltz et al, 2013;Attali, 2011). For example, Attali (2011) proposed a measure of differential use of words in higher and lower scoring essays defined similarly to GoodVsBad, without, however, considering the source text at all.…”

Section: Related Workmentioning

confidence: 99%

Content Importance Models for Scoring Writing From Sources

Klebanov

Madnani

Burstein

et al. 2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

View full text Add to dashboard Cite

Selection of information from external sources is an important skill assessed in educational measurement. We address an integrative summarization task used in an assessment of English proficiency for nonnative speakers applying to higher education institutions in the USA. We evaluate a variety of content importance models that help predict which parts of the source material should be selected by the test-taker in order to succeed on this task.

show abstract

“…Automated Essay Scoring (AES) systems constitute a relatively well-developed commercial technology for evaluation of students' written essays (Burstein et al 1998;Burstein 2003;Edelblut and Change 2004;Foltz et al 2013;Landauer et al 2003;Plakans and Gebril 2013;Rock 2007;Rudner et al 2006). They typically assign scores to essays on a 3-point or 4-point scale, and agree with human scorers as well as human scorers agree with each other.…”

Section: Automated Rubrics and Support For Source-based Writingmentioning

confidence: 99%