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CLUSTERING TECHNIQUES TO INVESTIGATE ENGAGEMENT AND PERFORMANCE IN ONLINE MATHEMATICS COURSES.

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Title: CLUSTERING TECHNIQUES TO INVESTIGATE ENGAGEMENT AND PERFORMANCE IN ONLINE MATHEMATICS COURSES.
Authors: Floris, Francesco1, Marchisio, Marina1, Roman, Fabio1, Sacchet, Matteo1, Rabellino, Sergio2
Source: Proceedings of the IADIS International Conference on Cognition & Exploratory Learning in Digital Age. 2022, p27-34. 8p.
Subject Terms: *Mathematics education, *Online education, *School enrollment, *Student attitudes, *Data analysis
Abstract: Among the various kinds of learning analytics emerging especially in the latest decade, clicking patterns cover a prominent role, fostered by their success in analyzing several types of data concerning activity on the web. They can be defined as sets of clicks performed by users, in which every set is treated as the basic unit. Few research has been performed on clicking patterns in educational contexts. In this paper, we perform analysis regarding clicks to an online course in Mathematics, aimed at allowing students to follow courses at a distance, both before and after enrolling at University. We used clustering techniques on students learning behavior, which have been defined for this research as visualizations of activities and resources of the course, to detect differences on students' grade according to their online learning behavior. Our results show that students tend to proceed on the course in both activities and resources. There is no correlation between participation and course grades, even if the most active students show higher scores. Moreover, patterns differ significantly according to the degree program of each student, showing the importance of tailored path. [ABSTRACT FROM AUTHOR]
Copyright of Proceedings of the IADIS International Conference on Cognition & Exploratory Learning in Digital Age is the property of International Association for Development of the Information Society (IADIS) 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: CLUSTERING TECHNIQUES TO INVESTIGATE ENGAGEMENT AND PERFORMANCE IN ONLINE MATHEMATICS COURSES.
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  Data: *<searchLink fieldCode="DE" term="%22Mathematics+education%22">Mathematics education</searchLink><br />*<searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br />*<searchLink fieldCode="DE" term="%22School+enrollment%22">School enrollment</searchLink><br />*<searchLink fieldCode="DE" term="%22Student+attitudes%22">Student attitudes</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink>
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  Data: Among the various kinds of learning analytics emerging especially in the latest decade, clicking patterns cover a prominent role, fostered by their success in analyzing several types of data concerning activity on the web. They can be defined as sets of clicks performed by users, in which every set is treated as the basic unit. Few research has been performed on clicking patterns in educational contexts. In this paper, we perform analysis regarding clicks to an online course in Mathematics, aimed at allowing students to follow courses at a distance, both before and after enrolling at University. We used clustering techniques on students learning behavior, which have been defined for this research as visualizations of activities and resources of the course, to detect differences on students' grade according to their online learning behavior. Our results show that students tend to proceed on the course in both activities and resources. There is no correlation between participation and course grades, even if the most active students show higher scores. Moreover, patterns differ significantly according to the degree program of each student, showing the importance of tailored path. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Proceedings of the IADIS International Conference on Cognition & Exploratory Learning in Digital Age is the property of International Association for Development of the Information Society (IADIS) 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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      – Code: eng
        Text: English
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        PageCount: 8
        StartPage: 27
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      – SubjectFull: Online education
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      – SubjectFull: School enrollment
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      – SubjectFull: Student attitudes
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      – TitleFull: CLUSTERING TECHNIQUES TO INVESTIGATE ENGAGEMENT AND PERFORMANCE IN ONLINE MATHEMATICS COURSES.
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              Text: 2022
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