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Student Performance on an E-Learning Platform: Mixed Method Approach.

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Title: Student Performance on an E-Learning Platform: Mixed Method Approach.
Authors: Rakic, Slavko1 slavkorakic@uns.ac.rs, Tasic, Nemanja1, Marjanovic, Ugljesa1, Softic, Selver2, Lüftenegger, Egon2, Turcin, Ioan2
Source: International Journal of Emerging Technologies in Learning. 2020, Vol. 15 Issue 2, p187-203. 17p.
Subject Terms: *Learning management system, *Educational technology, *Educational resources, Digital technology, Social network analysis, K-means clustering
Geographic Terms: Serbia
Abstract: E-learning is considered a leading application of digital technologies in educational systems. The aim of the paper is to explore the utilization and impact of digital technologies on an e-learning platform. For this purpose, research was conducted at the Moodle learning management system. Data from the e-learning platform were empirically evaluated in order to find key indicators of student performance in different courses. Student success with the elearning system was evaluated using a mixed-method: Social Network Analysis, K-Means Clustering, and Multiple Linear Regression. The research was conducted at the University of Novi Sad, Faculty of Technical Sciences, Serbia. The results indicate a significant relationship between the performance of students and the use of digital educational resources from the e-learning platform. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Student Performance on an E-Learning Platform: Mixed Method Approach.
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  Data: <searchLink fieldCode="AR" term="%22Rakic%2C+Slavko%22">Rakic, Slavko</searchLink><relatesTo>1</relatesTo><i> slavkorakic@uns.ac.rs</i><br /><searchLink fieldCode="AR" term="%22Tasic%2C+Nemanja%22">Tasic, Nemanja</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Marjanovic%2C+Ugljesa%22">Marjanovic, Ugljesa</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Softic%2C+Selver%22">Softic, Selver</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Lüftenegger%2C+Egon%22">Lüftenegger, Egon</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Turcin%2C+Ioan%22">Turcin, Ioan</searchLink><relatesTo>2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Emerging+Technologies+in+Learning%22">International Journal of Emerging Technologies in Learning</searchLink>. 2020, Vol. 15 Issue 2, p187-203. 17p.
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  Data: E-learning is considered a leading application of digital technologies in educational systems. The aim of the paper is to explore the utilization and impact of digital technologies on an e-learning platform. For this purpose, research was conducted at the Moodle learning management system. Data from the e-learning platform were empirically evaluated in order to find key indicators of student performance in different courses. Student success with the elearning system was evaluated using a mixed-method: Social Network Analysis, K-Means Clustering, and Multiple Linear Regression. The research was conducted at the University of Novi Sad, Faculty of Technical Sciences, Serbia. The results indicate a significant relationship between the performance of students and the use of digital educational resources from the e-learning platform. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 2020
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