grade:一个自动的简答评分系统

Dina H Alhamed, Aljawharah Mohammad Alajmi, Y. Alali, T. A. Alqahtani, M. R. Alnassar, Dina A. Alabbad
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引用次数: 0

摘要

在2019冠状病毒病大流行期间,大多数国家依靠电子学习来实施影响考试评估过程的社会距离政策。本项目旨在协助教师对ccsat课程的简答题进行评分。通过植入一个网站应用程序,教师可以使用该应用程序上传学生的答案,“iggrade”软件模型将对其进行评分。此外,该系统将通过节省时间和精力来减轻设施成员的工作量,并保证学生的客观评分。在这个项目中使用的模型是一个最先进的BERT神经网络模型以及BiLSTM层,该模型使用从以前的CIS 211课程的期中和期末考试中收集的数据集进行训练。数据集由(0,0.5,1)三个类别组成,大约有1,128个实例。“iggrade”测试获得了85.4%的准确率分数,证明了BERT作为NLP默认方法在简答评分过程中的优越性和独立于特征的独立性。CCS概念•计算方法•人工智能•自然语言处理
本文章由计算机程序翻译,如有差异,请以英文原文为准。
iGrade: an automated short answer grading system
During the COVID-19 pandemic, most countries rely on E-Learning to apply social distance policy which affects the exams evaluation process. This project aimed to assist instructors in grading the short answer questions for CCSIT courses. By implanting a website application that the instructors could use to upload the students' answers and the ‘iGrade” software model will grade it. Moreover, the system will reduce the workload on the facilities members by saving time and effort as well as guarantee an objective grading for students. The model used in this project is a state-of-the-art BERT Neural Network model along with layers of BiLSTM that was trained using a dataset that has been collected from previous midterm and final exams of the CIS 211 course. The dataset consists of three categories which are (0, 0.5, 1) with around 1,128 instances. The "iGrade" test obtained an accuracy score of 85,4%, demonstrating BERT's superiority and independence from features during short answer grading as a default method in NLP. CCS CONCEPTS • Computing methodologies • Artificial intelligence • Natural language processing
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