Online Student Feedback System Using Machine Learning

• 出版年: Zanco Journal of Pure and Applied Sciences
• 第一著者: Haider Abdula Haddad
• フォーマット: 論文
• 言語: 英語
• 出版事項: Salahaddin University-Erbil 2023-06-01
• 主題: feedback system; svm algorithm; machine learning; university dataset; online system
• オンライン･アクセス: https://zancojournal.su.edu.krd/index.php/JPAS/article/view/1233

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 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 methods. 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 student-learning performance.