Self-regulated learning support in technology enhanced learning environments: A reliability analysis of the SRL-S rubric

Advanced learning technologies have become a focal point in recent educational research, holding the promise of enhancing students' self-regulated learning (SRL) by facilitating various processes of planning, monitoring, performing, and reflecting upon learning experiences. However, concerns have arisen regarding the efficacy and design of technologies, the spectrum of possibilities for SRL support, and too ambiguous claims associated with these technologies. To address these uncertainties and to provide a platform for generating the more empirical evidence, Self-Regulated Learning Support (SRL-S) rubric was developed to facilitate the assessment of SRL support in technology-enhanced learning environments. It is grounded in established educational theory and proven empirical research results. This article presents a study that extends the application of the rubric to establish its reliability and validity, filling a gap in prior research. First, content, criterion-related, and construct validation were performed through international and interdisciplinary experts’ reviews. Subsequently, inter-rater and intra-rater reliability were assessed using Intraclass Correlation Coefficients and Cohens Kappa tests. The outcomes of these analysis demonstrated that the SRL-S is a reliable and valid instrument for assessing the levels of SRL support within learning environments. Additional implications for further research to support self-regulated learning are discussed.

Authors

Institutions

Publication Details

Journal
International Journal of Assessment Tools in Education
Published
2024-09-26
DOI
https://doi.org/10.21449/ijate.1502786
Citations
6
Primary Topic
Innovative Teaching and Learning Methods
Type
article
Field-Weighted Citation Impact
4.74
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Self-regulated learning support in technology enhanced learning environments: A reliability analysis of the SRL-S rubric

Slaviša Radović, Niels Seidel
6 citations
International Journal of Assessment Tools in Education
Innovative Teaching and Learning Methods
4.74
article

Self-regulated learning support in technology enhanced learning environments: A reliability analysis of the SRL-S rubric

Slaviša Radović, Niels Seidel
article en
6 citations

Abstract

Advanced learning technologies have become a focal point in recent educational research, holding the promise of enhancing students' self-regulated learning (SRL) by facilitating various processes of planning, monitoring, performing, and reflecting upon learning experiences. However, concerns have arisen regarding the efficacy and design of technologies, the spectrum of possibilities for SRL support, and too ambiguous claims associated with these technologies. To address these uncertainties and to provide a platform for generating the more empirical evidence, Self-Regulated Learning Support (SRL-S) rubric was developed to facilitate the assessment of SRL support in technology-enhanced learning environments. It is grounded in established educational theory and proven empirical research results. This article presents a study that extends the application of the rubric to establish its reliability and validity, filling a gap in prior research. First, content, criterion-related, and construct validation were performed through international and interdisciplinary experts’ reviews. Subsequently, inter-rater and intra-rater reliability were assessed using Intraclass Correlation Coefficients and Cohens Kappa tests. The outcomes of these analysis demonstrated that the SRL-S is a reliable and valid instrument for assessing the levels of SRL support within learning environments. Additional implications for further research to support self-regulated learning are discussed.

International Journal of Assessment Tools in EducationVol. 11(4)
FernUniversität in Hagen (DE)
Openalex Percentile: Top 5%
Innovative Teaching and Learning Methods
4.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.