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Student Engagement Patterns in a Blended Learning Environment: an Educational Data Mining Approach.

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Title: Student Engagement Patterns in a Blended Learning Environment: an Educational Data Mining Approach.
Authors: Nkomo, Larian M.1 (AUTHOR) larian.nkomo@otago.ac.nz, Nat, Muesser2 (AUTHOR)
Source: TechTrends: Linking Research & Practice to Improve Learning. Sep2021, Vol. 65 Issue 5, p808-817. 10p.
Subject Terms: *Student engagement, *Blended learning, *Classroom environment, *School environment, Data mining, Digital technology
Abstract: With various digital technologies increasingly integrated into higher education, understanding how students engage with such technologies has become vital. There are different ways to measure student engagement; however, self-reported measures such as questionnaires are predominantly used to understand student engagement. In contrast, this study utilises an Educational Data Mining (EDM) technique to discover students' engagement patterns (N = 54) in a Blended Learning (BL) environment. SimpleKmeans clustering technique is applied to students' learning data obtained from a BL environment and patterns of student engagement are identified. Findings suggest students engage differently with learning resources as students have different engagement patterns based on low, moderate and high engagement levels. The analysis of student generated data can help provide timely interventions that enhance student engagement. Furthermore, educators should concentrate on best practices in order to engage students bearing in mind that students engage differently with learning resources. [ABSTRACT FROM AUTHOR]
Copyright of TechTrends: Linking Research & Practice to Improve Learning is the property of Springer Nature 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 Engagement Patterns in a Blended Learning Environment: an Educational Data Mining Approach.
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  Data: With various digital technologies increasingly integrated into higher education, understanding how students engage with such technologies has become vital. There are different ways to measure student engagement; however, self-reported measures such as questionnaires are predominantly used to understand student engagement. In contrast, this study utilises an Educational Data Mining (EDM) technique to discover students' engagement patterns (N = 54) in a Blended Learning (BL) environment. SimpleKmeans clustering technique is applied to students' learning data obtained from a BL environment and patterns of student engagement are identified. Findings suggest students engage differently with learning resources as students have different engagement patterns based on low, moderate and high engagement levels. The analysis of student generated data can help provide timely interventions that enhance student engagement. Furthermore, educators should concentrate on best practices in order to engage students bearing in mind that students engage differently with learning resources. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of TechTrends: Linking Research & Practice to Improve Learning is the property of Springer Nature 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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