Automated Scoring of Summary-Writing Tasks Designed to Measure Reading Comprehension
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| Title: | Automated Scoring of Summary-Writing Tasks Designed to Measure Reading Comprehension |
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| Language: | English |
| Authors: | Madnani, Nitin (ORCID |
| Source: | Grantee Submission. 2013Paper presented at the Workshop on Innovative Use of Natural Language Processing for Building Educational Applications (8th, 2013). |
| Peer Reviewed: | Y |
| Page Count: | 7 |
| Publication Date: | 2013 |
| Sponsoring Agency: | Institute of Education Sciences (ED) |
| Contract Number: | R305F100005 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Elementary Education Secondary Education Grade 6 Intermediate Grades Middle Schools Grade 7 Junior High Schools Grade 9 High Schools |
| Descriptors: | Computer Assisted Testing, Scoring, Writing Evaluation, Reading Comprehension, Elementary School Students, Secondary School Students, Grade 6, Grade 7, Grade 9, Automation |
| Abstract: | We introduce a cognitive framework for measuring reading comprehension that includes the use of novel summary-writing tasks. We derive NLP features from the holistic rubric used to score the summaries written by students for such tasks and use them to design a preliminary, automated scoring system. Our results show that the automated approach performs very well on summaries written by students for two different passages. [This manuscript is an early draft of a paper published in: "Proceedings of the 8th Workshop on Innovative Use of Natural Language Processing for Building Educational Applications" (pp. 163-168). Atlanta, GA: Association for Computational Linguistics.] |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2020 |
| Accession Number: | ED603960 |
| Database: | ERIC |
| Abstract: | We introduce a cognitive framework for measuring reading comprehension that includes the use of novel summary-writing tasks. We derive NLP features from the holistic rubric used to score the summaries written by students for such tasks and use them to design a preliminary, automated scoring system. Our results show that the automated approach performs very well on summaries written by students for two different passages. [This manuscript is an early draft of a paper published in: "Proceedings of the 8th Workshop on Innovative Use of Natural Language Processing for Building Educational Applications" (pp. 163-168). Atlanta, GA: Association for Computational Linguistics.] |
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