Capturing the Complex: An Intraindividual Temporal Network Analysis of Learning Resource Regulation
Understanding a learner’s resources as a system of interacting components, the success of a learning process is determined by the effectiveness of their interactions. Theoretical assumptions and empirical findings clearly show the importance of resource availability in learning systems but do not su...
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MDPI AG
2025-06-01
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| Series: | Education Sciences |
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| author | Bettina Harder Nick Naujoks-Schober Manuel D. S. Hopp |
| author_facet | Bettina Harder Nick Naujoks-Schober Manuel D. S. Hopp |
| author_sort | Bettina Harder |
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| description | Understanding a learner’s resources as a system of interacting components, the success of a learning process is determined by the effectiveness of their interactions. Theoretical assumptions and empirical findings clearly show the importance of resource availability in learning systems but do not sufficiently consider the individuality or the temporal and situational aspects of resource regulation. Therefore, the current study addresses the complex interplay between learning resources (educational and learning capitals) in an individual learner (<i>N</i> = 1) by utilizing multivariate time series data of a 50-day vocabulary learning process with daily assessments of learning resource availability, performance, learning duration, and stress. We draw on methods of psychometric network analysis, modeling all variables in simultaneous interaction and allowing predictions between all variables from measuring point to measuring point (temporal dynamics). Specifically, using a Graphical Vector Autoregressive (graphicalVAR) model, yielding a contemporaneous and a temporal dynamics network model, we identified pivotal resources in regulating the student’s learning processes and outcomes, including resources with strong connections to other variables, intermediary resources, and resources maintaining the system’s homeostasis. This innovative approach has possible applications as a diagnostic tool that lays the foundation for tailored interventions. |
| format | Article |
| id | doaj-art-f76555255a8c40718d08a76f46c52f15 |
| institution | Matheson Library |
| issn | 2227-7102 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj-art-f76555255a8c40718d08a76f46c52f152025-06-25T13:44:38ZengMDPI AGEducation Sciences2227-71022025-06-0115672810.3390/educsci15060728Capturing the Complex: An Intraindividual Temporal Network Analysis of Learning Resource RegulationBettina Harder0Nick Naujoks-Schober1Manuel D. S. Hopp2Department of Psychology, Friedrich-Alexander Universität Erlangen-Nürnberg, 90478 Nürnberg, GermanyDepartment of Psychology, Friedrich-Alexander Universität Erlangen-Nürnberg, 90478 Nürnberg, GermanyHector Research Institute of Education Sciences and Psychology, University of Tübingen, 72072 Tübingen, GermanyUnderstanding a learner’s resources as a system of interacting components, the success of a learning process is determined by the effectiveness of their interactions. Theoretical assumptions and empirical findings clearly show the importance of resource availability in learning systems but do not sufficiently consider the individuality or the temporal and situational aspects of resource regulation. Therefore, the current study addresses the complex interplay between learning resources (educational and learning capitals) in an individual learner (<i>N</i> = 1) by utilizing multivariate time series data of a 50-day vocabulary learning process with daily assessments of learning resource availability, performance, learning duration, and stress. We draw on methods of psychometric network analysis, modeling all variables in simultaneous interaction and allowing predictions between all variables from measuring point to measuring point (temporal dynamics). Specifically, using a Graphical Vector Autoregressive (graphicalVAR) model, yielding a contemporaneous and a temporal dynamics network model, we identified pivotal resources in regulating the student’s learning processes and outcomes, including resources with strong connections to other variables, intermediary resources, and resources maintaining the system’s homeostasis. This innovative approach has possible applications as a diagnostic tool that lays the foundation for tailored interventions.https://www.mdpi.com/2227-7102/15/6/728learning resourceseducational and learning capitalsregulationinteractiontime seriesintraindividual temporal network analysis |
| spellingShingle | Bettina Harder Nick Naujoks-Schober Manuel D. S. Hopp Capturing the Complex: An Intraindividual Temporal Network Analysis of Learning Resource Regulation Education Sciences learning resources educational and learning capitals regulation interaction time series intraindividual temporal network analysis |
| title | Capturing the Complex: An Intraindividual Temporal Network Analysis of Learning Resource Regulation |
| title_full | Capturing the Complex: An Intraindividual Temporal Network Analysis of Learning Resource Regulation |
| title_fullStr | Capturing the Complex: An Intraindividual Temporal Network Analysis of Learning Resource Regulation |
| title_full_unstemmed | Capturing the Complex: An Intraindividual Temporal Network Analysis of Learning Resource Regulation |
| title_short | Capturing the Complex: An Intraindividual Temporal Network Analysis of Learning Resource Regulation |
| title_sort | capturing the complex an intraindividual temporal network analysis of learning resource regulation |
| topic | learning resources educational and learning capitals regulation interaction time series intraindividual temporal network analysis |
| url | https://www.mdpi.com/2227-7102/15/6/728 |
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