Comparative Analysis of Clustering Algorithms and Moodle Plugin for Creation of Student Heterogeneous Groups in Online University Courses
Online learning environments such as e-learning platforms are often used to encourage collaborative activities amongst students. In this context, group work is often used to improve the learning outcomes. Group formation is often performed randomly since university courses can be composed of a large...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/13/5800 |
| _version_ | 1797529180555968512 |
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| author | Giacomo Nalli Daniela Amendola Andrea Perali Leonardo Mostarda |
| author_facet | Giacomo Nalli Daniela Amendola Andrea Perali Leonardo Mostarda |
| author_sort | Giacomo Nalli |
| collection | DOAJ |
| description | Online learning environments such as e-learning platforms are often used to encourage collaborative activities amongst students. In this context, group work is often used to improve the learning outcomes. Group formation is often performed randomly since university courses can be composed of a large number of students. While random formation saves time and resources, the student heterogeneity in terms of learning capabilities is not guaranteed. Although advanced e-learning platforms such as Moodle are widely used, they lack plugins that allow the automatic formation of heterogeneous groups of students. This work proposes a novel intelligent plugin for Moodle that allows the creation of heterogeneous groups by using Machine Learning. This intelligent application can be used in order to improve the students’ performance in collaborative activities. Our machine learning approach first uses clustering algorithms on Moodle data to identify homogeneous groups that are composed of students having similar behavior. Heterogeneous groups are then created by combining students selected from different homogeneous groups. To this end, a novel algorithm and the corresponding software, which allow the creation of heterogeneous groups, have been developed. We have implemented our approach by realizing a Moodle plugin where teachers can create heterogeneous groups. |
| first_indexed | 2024-03-10T10:10:05Z |
| format | Article |
| id | doaj.art-c85d682ea57149c687e735e99c01ac58 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:10:05Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c85d682ea57149c687e735e99c01ac582023-11-22T01:16:55ZengMDPI AGApplied Sciences2076-34172021-06-011113580010.3390/app11135800Comparative Analysis of Clustering Algorithms and Moodle Plugin for Creation of Student Heterogeneous Groups in Online University CoursesGiacomo Nalli0Daniela Amendola1Andrea Perali2Leonardo Mostarda3Computer Science Department, University of Camerino, 62032 Camerino, ItalyBioscience and Biotechnology Department, University of Camerino, 62032 Camerino, ItalyPhysics Unit, School of Pharmacy, University of Camerino, 62032 Camerino, ItalyComputer Science Department, University of Camerino, 62032 Camerino, ItalyOnline learning environments such as e-learning platforms are often used to encourage collaborative activities amongst students. In this context, group work is often used to improve the learning outcomes. Group formation is often performed randomly since university courses can be composed of a large number of students. While random formation saves time and resources, the student heterogeneity in terms of learning capabilities is not guaranteed. Although advanced e-learning platforms such as Moodle are widely used, they lack plugins that allow the automatic formation of heterogeneous groups of students. This work proposes a novel intelligent plugin for Moodle that allows the creation of heterogeneous groups by using Machine Learning. This intelligent application can be used in order to improve the students’ performance in collaborative activities. Our machine learning approach first uses clustering algorithms on Moodle data to identify homogeneous groups that are composed of students having similar behavior. Heterogeneous groups are then created by combining students selected from different homogeneous groups. To this end, a novel algorithm and the corresponding software, which allow the creation of heterogeneous groups, have been developed. We have implemented our approach by realizing a Moodle plugin where teachers can create heterogeneous groups.https://www.mdpi.com/2076-3417/11/13/5800e-learningmachine learningmoodleclusteringheterogeneous groups |
| spellingShingle | Giacomo Nalli Daniela Amendola Andrea Perali Leonardo Mostarda Comparative Analysis of Clustering Algorithms and Moodle Plugin for Creation of Student Heterogeneous Groups in Online University Courses Applied Sciences e-learning machine learning moodle clustering heterogeneous groups |
| title | Comparative Analysis of Clustering Algorithms and Moodle Plugin for Creation of Student Heterogeneous Groups in Online University Courses |
| title_full | Comparative Analysis of Clustering Algorithms and Moodle Plugin for Creation of Student Heterogeneous Groups in Online University Courses |
| title_fullStr | Comparative Analysis of Clustering Algorithms and Moodle Plugin for Creation of Student Heterogeneous Groups in Online University Courses |
| title_full_unstemmed | Comparative Analysis of Clustering Algorithms and Moodle Plugin for Creation of Student Heterogeneous Groups in Online University Courses |
| title_short | Comparative Analysis of Clustering Algorithms and Moodle Plugin for Creation of Student Heterogeneous Groups in Online University Courses |
| title_sort | comparative analysis of clustering algorithms and moodle plugin for creation of student heterogeneous groups in online university courses |
| topic | e-learning machine learning moodle clustering heterogeneous groups |
| url | https://www.mdpi.com/2076-3417/11/13/5800 |
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