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Classifying relevant video tutorials for the school’s learning management system using support vector machine algorithm

تصنيف مقاطع الفيديو التعليمية ذات الصلة لنظام إدارة التعلم في المدرسة باستخدام خوارزمية آلة ناقلات الدعم
Authors: Castro Mayleen Dorcas Bondoc; Tumibay Gilbert Malawit;

Classifying relevant video tutorials for the school’s learning management system using support vector machine algorithm

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.

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Keywords

Artificial intelligence, Support vector machine, Education, Engineering, Machine learning, Aside, Educational Data Mining, Learning Management Systems (LMS); Machine Learning (ML) Algorithms; Support Vector Machine (SVM); Video Tutorials; Classification; Regression Analysis, Data-driven Education, Learning Management, Electrical and Computer Engineering, Computer science, Learning Analytics, Computer Science Applications, Process (computing), Algorithm, Operating system, Online Learning, Multimedia, Literature, Computer Science, Physical Sciences, Educational Data Mining and Learning Analytics, Art

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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