Online Student Feedback System Using Machine Learning

Haider Abdula, Haddad
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Abstract

In order to develop plans to enhance the teaching experience, student feedback data analysis is a very good tool to enhance the relationship between teachers and students. This research is to present an analytical model for data from student feedback systems to improve the quality of teaching in academic institutions and universities. The developed system in this research uses the lexical analysis algorithm Support Vector Machine (SVM), which has the best accuracy and is one of the machine learning algorithms that can provides textual feedback and useful insights into the overall quality of teaching to improve teaching performance. In this research an online system for student feedback was created. The system is used to get feedback from students about teachers and their methods of teaching. The system uses a large database to collect a large dataset from all students at different colleges at the university level. The system administrators include staff on the college levels from all colleges. All students will be provided with unique usernames and passwords to log in to the system. Among the main tasks for the system administrator is to create classes and to create feedback questions that are designed in two questionnaire forms. The first questionnaire form is about academic questions that are related to the quality of teaching the academic subject. The second questionnaire form is the questions that are related to general education for students. The textual analysis in this system is provided using the (SVM) lexical analysis algorithm, which has the best accuracy but it requires more training time for large data sets to classify the text. The student feedback system developed and used in this research proved to be an excellent tool to improve the academic and educational status of the university. It also helps reduce manual labor in collecting, storing and analyzing feedback data. This system is an efficient way to provide qualitative feedback to teachers that improves
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基于机器学习的在线学生反馈系统
为了制定计划来增强教学体验,学生反馈数据分析是增强师生关系的一个很好的工具。本研究旨在为来自学生反馈系统的数据提供一个分析模型,以提高学术机构和大学的教学质量。本研究中开发的系统使用了词汇分析算法支持向量机(SVM),该算法具有最佳的准确性,是一种可以提供文本反馈和对教学整体质量的有用见解以提高教学性能的机器学习算法。在这项研究中,创建了一个学生反馈的在线系统。该系统用于获得学生对教师及其教学方法的反馈。该系统使用一个大型数据库来收集来自大学不同学院的所有学生的大型数据集。系统管理员包括来自所有学院的学院级别的工作人员。所有学生都将获得唯一的用户名和密码以登录系统。系统管理员的主要任务之一是创建类和创建以两种问卷形式设计的反馈问题。第一份问卷是关于与学科教学质量有关的学术问题。第二种问卷形式是与学生通识教育相关的问题。该系统中的文本分析是使用(SVM)词汇分析算法提供的,该算法具有最佳的准确性,但对于大数据集来说,对文本进行分类需要更多的训练时间。本研究中开发和使用的学生反馈系统被证明是提高大学学术和教育地位的优秀工具。它还有助于减少收集、存储和分析反馈数据的人工劳动。该系统是向教师提供质量反馈的有效方式,可改进
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64
审稿时长
8 weeks
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