Classifying relevant video tutorials for the school’s learning management system using support vector machine algorithm
1 Bulacan State University, City of Malolos, Bulacan, Philippines.
2 Angeles University Foundation, Graduate School, Angeles City, Philippines.
Research Article
Global Journal of Engineering and Technology Advances, 2020, 02(03), 001-009.
Article DOI: 10.30574/gjeta.2020.2.3.0011
Publication history:
Received on 18 February 2020; revised on 24 February 2020; accepted on 25 February 2020
Abstract:
Today many schools, universities and institutions recognize the necessity and importance of using Learning Management Systems (LMS) as part of their educational services. This research work has applied LMS in the teaching and learning process of Bulacan State University (BulSU) Graduate School (GS) Program that enhances the face-to-face instruction with online components. The researchers uses an LMS that provides educators a platform that can motivate and engage students to new educational environment through manage online classes. The LMS allows educators to distribute information, manage learning materials, assignments, quizzes, and communications. Aside from the basic functions of the LMS, the researchers uses Machine Learning (ML) Algorithms applying Support Vector Machine (SVM) that will classify and identify the best related videos per topic. SVM is a supervised machine learning algorithm that analyzes data for classification and regression analysis by Maity [1]. The results of this study showed that integration of video tutorials in LMS can significantly contribute knowledge and skills in the learning process of the students.
Keywords:
Learning Management Systems (LMS); Machine Learning (ML) Algorithms; Support Vector Machine (SVM); Video Tutorials; Classification; Regression Analysis
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Copyright information:
Copyright © 2020 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0