Automatic Scoring of an Analytical Response-To-Text Assessment

Rahimi, Zahra; Litman, Diane; Correnti, Richard; Matsumura, Lindsay Clare; Wang, Elaine; Kisa, Zahid

doi:10.1007/978-3-319-07221-0_76

Cited by 14 publications

(31 citation statements)

References 11 publications

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“…Table 4: A sub-list of manually extracted a) topic words and b) specific expressions for three sample topics. They are manually provided by experts in (Rahimi et al, 2014). Some of the stop-words might have been removed from the expressions by experts.…”

Section: Experimental Tools and Methodsmentioning

confidence: 99%

“…Some of the stop-words might have been removed from the expressions by experts. We compare results for models that extracted features from topical components with a baseline model which uses the top 500 unigrams as features (chosen based on a chi-squared feature selection method), and with an upper-bound model which is the best model reported in (Rahimi et al, 2014). The only difference between our model and the upper-bound model is that in our model the topical components were extracted automatically instead of manually.…”

Section: Experimental Tools and Methodsmentioning

confidence: 99%

“…We use the same set of primarily rubric-based features introduced in (Rahimi et al, 2014) to score the Evidence dimension of RTA:…”

Section: Featuresmentioning

confidence: 99%

“…Then the Millennium Village project can move on to help other countries in need. Table 1: An excerpt from the source text, the prompt, and a high-scoring essay with highlighted evidence (Rahimi et al, 2014). (Correnti et al, 2012;Correnti et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Automatic scoring of the Evidence dimension of the RTA was previously investigated in (Rahimi et al, 2014). The Evidence dimension evaluates how well students use selected details from a text to support and extend a key idea.…”

Section: Introductionmentioning

confidence: 99%

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Automatically Extracting Topical Components for a Response-to-Text Writing Assessment

Rahimi

Litman

2016

Proceedings of the 11th Workshop on Innovative Use of NLP For Building Educational Applications

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We investigate automatically extracting multiword topical components to replace information currently provided by experts that is used to score the Evidence dimension of a writing in response to text assessment. Our goal is to reduce the amount of expert effort and improve the scalability of an automatic scoring system. Experimental results show that scoring performance using automatically extracted data-driven topical components is promising.

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Section: Experimental Tools and Methodsmentioning

confidence: 99%

Section: Experimental Tools and Methodsmentioning

confidence: 99%

“…We use the same set of primarily rubric-based features introduced in (Rahimi et al, 2014) to score the Evidence dimension of RTA:…”

Section: Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automatically Extracting Topical Components for a Response-to-Text Writing Assessment

Rahimi

Litman

2016

Proceedings of the 11th Workshop on Innovative Use of NLP For Building Educational Applications

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Automated Scoring of Students’ Use of Text Evidence in Writing

Correnti

Matsumura

Wang

et al. 2019

Reading Research Quarterly

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Despite the importance of analytic text‐based writing, relatively little is known about how to teach to this important skill. A persistent barrier to conducting research that would provide insight on best practices for teaching this form of writing is a lack of outcome measures that assess students’ analytic text‐based writing development and that are feasible to implement at scale. Automated essay‐scoring (AES) technologies offer one potential approach to increasing the feasibility of research in this area, provided that the scores yield information about substantive dimensions of writing aligned to new standards and are sensitive to variation in literacy instruction. The authors describe an approach to using AES technologies to provide information about students’ skills at marshaling text evidence in the upper elementary grades. Specifically, the authors examined 1,529 responses to a response‐to‐text assessment (RTA) from 65 fifth‐ and sixth‐grade language arts classrooms, from which the authors also collected data on instruction via logs, text‐based writing assignments, and surveys. Through correlational, univariate, and multilevel multivariate analyses, the authors found validity evidence supporting automated scoring of the RTA: The authors found close correspondence of human and AES scores, alignment of AES scores with components of instruction that the authors expected would predict variation in students’ writing quality, and association between AES scores and other expected measures of student achievement. These findings provide encouraging evidence that AES technologies as applied to the RTA can generate valid inferences about students’ ability to marshal text evidence in writing and, thus, could be a useful tool for advancing large‐scale writing research.

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Text mining in education

Mello

Andre

Pinheiro

et al. 2019

WIREs Data Min & Knowl

148

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The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends. This article is categorized under: Application Areas > Education and Learning Ensemble Methods > Text Mining

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Automatic Scoring of an Analytical Response-To-Text Assessment

Cited by 14 publications

References 11 publications

Automatically Extracting Topical Components for a Response-to-Text Writing Assessment

Automatically Extracting Topical Components for a Response-to-Text Writing Assessment

Automated Scoring of Students’ Use of Text Evidence in Writing

Text mining in education

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