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Predicting Evaluations of Essay by Computational Graph-Based Features.

Liping Yang1, Tao Xin1, Canxi Cao1

  • 1Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China.

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|December 7, 2020
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Summary
This summary is machine-generated.

Graph-based features effectively predict Chinese middle school students' essay quality, offering a systematic approach to evaluating writing. This method reliably assesses essays based on scoring rubrics, supplementing human evaluation.

Keywords:
automatic essay scoringgraph-based featuresreliabilityscoring rubricwriting ability

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Area of Science:

  • Educational Technology
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Evaluating student essays objectively using writing criteria is a persistent challenge.
  • The advent of automatic essay scoring raises questions about systematic quality assessment via scoring rubrics.

Purpose of the Study:

  • To investigate the efficacy of graph-based features in predicting the writing quality of Chinese middle school students' essays.
  • To determine if graph-based features enable systematic essay evaluation aligned with scoring rubrics.

Main Methods:

  • Utilized an innovative set of graph-based features categorized into basic characteristics, main idea, essay content, and essay development.
  • Employed these features to predict essay quality for Chinese middle school students.

Main Results:

  • Graph-based features demonstrated significantly superior performance in predicting human essay scores compared to baseline features.
  • The study confirmed the predictive power of graph-based features for essay quality assessment.

Conclusions:

  • Graph-based features provide a reliable and systematic method for evaluating essay quality according to scoring rubrics.
  • This approach serves as a viable alternative or supplement to traditional human essay evaluation.