FACToGRADE: Automated Essay Scoring System

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI:10.1109/IAICT55358.2022.9887447

Lyla B. Das, C. V. Raghu, G. Jagadanand, Ritu Ann Roy George, Priyamvada Yashasawi, N. Kumaran, Vinay Kumar Patnaik

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引用次数: 1

Abstract

The significance of technology has exponentially grown in this increasingly virtual world, making online learning and evaluation the new normal. In the evaluation of writing assignments, many existing automated methods either focus on semantics or machine-learned features alone. In our project, we incorporate content analysis with structural analysis to provide a complete grading system. Also, revision and feedback are essential aspects of the writing process, with the help of which, students may increase their writing quality. Here, Automated Essay Scoring (AES) systems can be very useful as they can provide the student with a score as well as a feedback within seconds. Below we present an automated scoring system, built using the concepts of Long Short Term Memory (LSTM) and Entity Detection, incorporating a User Interface to input an essay and obtain its score along with the breakdown analysis of the essay.

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FACToGRADE:自动作文评分系统

在这个日益虚拟的世界里，技术的重要性呈指数级增长，使在线学习和评估成为新常态。在写作作业的评估中，许多现有的自动化方法要么只关注语义，要么只关注机器学习的特征。在我们的项目中，我们将内容分析与结构分析相结合，提供了一个完整的评分体系。此外，修改和反馈是写作过程中必不可少的方面，有了它们的帮助，学生可以提高他们的写作质量。在这里，自动作文评分(AES)系统非常有用，因为它们可以在几秒钟内为学生提供分数和反馈。下面我们介绍一个自动评分系统，使用长短期记忆(LSTM)和实体检测的概念构建，结合用户界面输入文章并获得其分数以及文章的细分分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

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