Introduction to the SRL-S Rubric for Evaluation of Innovative Higher Educational Technology for Self-Regulated Learning.
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| Title: | Introduction to the SRL-S Rubric for Evaluation of Innovative Higher Educational Technology for Self-Regulated Learning. |
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| Authors: | Radović, Slaviša1 slavisa.radovic@fernuni-hagen.de, Seidel, Niels1 niels.seidel@fernuni-hagen.de |
| Source: | Innovative Higher Education. Aug2025, Vol. 50 Issue 4, p1169-1202. 34p. |
| Subject Terms: | *Scoring rubrics, *Higher education, *Educational technology, *Self-regulated learning, Data mining |
| Abstract: | The integration of advanced learning analytics and data-mining technology into higher education has brought various opportunities and challenges, particularly in enhancing students' self-regulated learning (SRL) skills. Analyzing developed features for SRL support, it has become evident that SRL support is not a binary concept but rather a continuum, ranging from limited to advanced levels of SRL support. This article introduces the rubric, designed to evaluate the degree of self-regulated learning support available within technology enhanced learning environments. Following rubric design best practices, we took a multifaceted methodological approach to ensure rubric validity and reliability: consulting Zimmerman's theoretical model, comparing technological features distilled from empirical studies that demonstrated significant effectiveness, consulting SRL experts, and iterative development and feedback. Across three phases of SRL the rubrics describe evaluation criteria and in detail define performance levels (Limited, Moderate and Advance). By employing the rubric, educators and researchers can 1) gain insights into the extent of implemented SRL approaches, 2) further develop SRL support of learning environments, and 3) better support students on their journey towards becoming self-regulated learners. Finally, the reliability analysis demonstrated a high degree of agreement among different raters evaluating the same course, indicating that the rubric is a reliable tool for obtaining relevant evaluations of SRL support in higher education. We conclude by discussing the significance of the rubric in promoting self-regulated learning within the current pedagogical and technological landscape. [ABSTRACT FROM AUTHOR] |
| Copyright of Innovative Higher Education 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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| Header | DbId: ehh DbLabel: Education Research Complete An: 187381764 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Introduction to the SRL-S Rubric for Evaluation of Innovative Higher Educational Technology for Self-Regulated Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Radović%2C+Slaviša%22">Radović, Slaviša</searchLink><relatesTo>1</relatesTo><i> slavisa.radovic@fernuni-hagen.de</i><br /><searchLink fieldCode="AR" term="%22Seidel%2C+Niels%22">Seidel, Niels</searchLink><relatesTo>1</relatesTo><i> niels.seidel@fernuni-hagen.de</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Innovative+Higher+Education%22">Innovative Higher Education</searchLink>. Aug2025, Vol. 50 Issue 4, p1169-1202. 34p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Scoring+rubrics%22">Scoring rubrics</searchLink><br />*<searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Self-regulated+learning%22">Self-regulated learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The integration of advanced learning analytics and data-mining technology into higher education has brought various opportunities and challenges, particularly in enhancing students' self-regulated learning (SRL) skills. Analyzing developed features for SRL support, it has become evident that SRL support is not a binary concept but rather a continuum, ranging from limited to advanced levels of SRL support. This article introduces the rubric, designed to evaluate the degree of self-regulated learning support available within technology enhanced learning environments. Following rubric design best practices, we took a multifaceted methodological approach to ensure rubric validity and reliability: consulting Zimmerman's theoretical model, comparing technological features distilled from empirical studies that demonstrated significant effectiveness, consulting SRL experts, and iterative development and feedback. Across three phases of SRL the rubrics describe evaluation criteria and in detail define performance levels (Limited, Moderate and Advance). By employing the rubric, educators and researchers can 1) gain insights into the extent of implemented SRL approaches, 2) further develop SRL support of learning environments, and 3) better support students on their journey towards becoming self-regulated learners. Finally, the reliability analysis demonstrated a high degree of agreement among different raters evaluating the same course, indicating that the rubric is a reliable tool for obtaining relevant evaluations of SRL support in higher education. We conclude by discussing the significance of the rubric in promoting self-regulated learning within the current pedagogical and technological landscape. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Innovative Higher Education 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10755-024-09771-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 34 StartPage: 1169 Subjects: – SubjectFull: Scoring rubrics Type: general – SubjectFull: Higher education Type: general – SubjectFull: Educational technology Type: general – SubjectFull: Self-regulated learning Type: general – SubjectFull: Data mining Type: general Titles: – TitleFull: Introduction to the SRL-S Rubric for Evaluation of Innovative Higher Educational Technology for Self-Regulated Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Radović, Slaviša – PersonEntity: Name: NameFull: Seidel, Niels IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 07425627 Numbering: – Type: volume Value: 50 – Type: issue Value: 4 Titles: – TitleFull: Innovative Higher Education Type: main |
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