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Nonlinear Effort-Time Dynamics of Student Engagement in a Web-Based Learning Platform: A Person-Oriented Transition Analysis

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Title: Nonlinear Effort-Time Dynamics of Student Engagement in a Web-Based Learning Platform: A Person-Oriented Transition Analysis
Language: English
Authors: Elissavet Papageorgiou (ORCID 0000-0001-6995-7685), Jacqueline Wong (ORCID 0000-0002-5387-7696), Mohammad Khalil (ORCID 0000-0002-6860-4404), Annoesjka J. Cabo (ORCID 0000-0002-8305-9993)
Source: Journal of Learning Analytics. 2025 12(2):237-258.
Availability: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
Peer Reviewed: Y
Page Count: 53
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Learner Engagement, Student Behavior, Electronic Learning, Web Based Instruction, Foreign Countries, College Students, Learning Analytics, Behavior Patterns, Time Factors (Learning), Behavior Change
Geographic Terms: Netherlands
ISSN: 1929-7750
Abstract: Behavioural engagement as a predictor of academic success hinges on the interplay between effort and time. Exploring the longitudinal development of engagement is vital for understanding adaptations in learning behaviour and informing educational interventions. However, person-oriented longitudinal studies on student engagement are scarce. Moreover, online engagement metrics are rarely grounded in theory and often result in simplified descriptions overlooking the complexity of engagement processes. This study applies a theory-based operationalization of behavioural engagement to examine the log data of 236 students in a web-based learning platform. We explored (1) whether weekly profiles based on distinct engagement patterns can be identified and (2) how students transition across profiles over time. Hierarchical clustering yielded one Inactive and six active profiles (Fast-Learners, Regular-Learners, Average-Engagement, Minimalists, Struggling-Learners, and Procrastinators). Results suggest heterogeneity in profile emergence, with effective engagement characterized by alignment with the course deadlines. Process mining revealed changes in profile membership across weeks. Profile transitions revealed relative stability among effective groups and greater fluctuation among low-time profiles. By investigating the complexity and temporality of engagement in online learning, our findings provide insights for developing personalized learning support through training artificial intelligence applications and informing learning analytics dashboards.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1483354
Database: ERIC
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  Data: Nonlinear Effort-Time Dynamics of Student Engagement in a Web-Based Learning Platform: A Person-Oriented Transition Analysis
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  Data: <searchLink fieldCode="AR" term="%22Elissavet+Papageorgiou%22">Elissavet Papageorgiou</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6995-7685">0000-0001-6995-7685</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jacqueline+Wong%22">Jacqueline Wong</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5387-7696">0000-0002-5387-7696</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mohammad+Khalil%22">Mohammad Khalil</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6860-4404">0000-0002-6860-4404</externalLink>)<br /><searchLink fieldCode="AR" term="%22Annoesjka+J%2E+Cabo%22">Annoesjka J. Cabo</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8305-9993">0000-0002-8305-9993</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Learning+Analytics%22"><i>Journal of Learning Analytics</i></searchLink>. 2025 12(2):237-258.
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  Data: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
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  Data: <searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Web+Based+Instruction%22">Web Based Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+Patterns%22">Behavior Patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Time+Factors+%28Learning%29%22">Time Factors (Learning)</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+Change%22">Behavior Change</searchLink>
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  Data: 1929-7750
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  Label: Abstract
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  Data: Behavioural engagement as a predictor of academic success hinges on the interplay between effort and time. Exploring the longitudinal development of engagement is vital for understanding adaptations in learning behaviour and informing educational interventions. However, person-oriented longitudinal studies on student engagement are scarce. Moreover, online engagement metrics are rarely grounded in theory and often result in simplified descriptions overlooking the complexity of engagement processes. This study applies a theory-based operationalization of behavioural engagement to examine the log data of 236 students in a web-based learning platform. We explored (1) whether weekly profiles based on distinct engagement patterns can be identified and (2) how students transition across profiles over time. Hierarchical clustering yielded one Inactive and six active profiles (Fast-Learners, Regular-Learners, Average-Engagement, Minimalists, Struggling-Learners, and Procrastinators). Results suggest heterogeneity in profile emergence, with effective engagement characterized by alignment with the course deadlines. Process mining revealed changes in profile membership across weeks. Profile transitions revealed relative stability among effective groups and greater fluctuation among low-time profiles. By investigating the complexity and temporality of engagement in online learning, our findings provide insights for developing personalized learning support through training artificial intelligence applications and informing learning analytics dashboards.
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      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: Student Behavior
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      – SubjectFull: Electronic Learning
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      – SubjectFull: Time Factors (Learning)
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      – SubjectFull: Netherlands
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      – TitleFull: Nonlinear Effort-Time Dynamics of Student Engagement in a Web-Based Learning Platform: A Person-Oriented Transition Analysis
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