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] |
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| Database: | Education Research Complete |
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| 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] |
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| ISSN: | 07425627 |
| DOI: | 10.1007/s10755-024-09771-z |
