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Self-reported and digital-trace measures of computer science students’ self-regulated learning in blended course designs
This study investigated the extent to which self-report and digital-trace measures of students’ self-regulated learning in blended course designs align with each other amongst 145 first-year computer science students in a blended “computer systems” course. A self-reported Motivated Strategies for Le...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10037358/ https://www.ncbi.nlm.nih.gov/pubmed/37361755 http://dx.doi.org/10.1007/s10639-023-11698-5 |
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author | Han, Feifei Ellis, Robert A. |
author_facet | Han, Feifei Ellis, Robert A. |
author_sort | Han, Feifei |
collection | PubMed |
description | This study investigated the extent to which self-report and digital-trace measures of students’ self-regulated learning in blended course designs align with each other amongst 145 first-year computer science students in a blended “computer systems” course. A self-reported Motivated Strategies for Learning Questionnaire was used to measure students’ self-efficacy, intrinsic motivation, test anxiety, and use of self-regulated learning strategies. Frequencies of interactions with six different online learning activities were digital-trace measures of students’ online learning interactions. Students’ course marks were used to represent their academic performance. SPSS 28 was used to analyse the data. A hierarchical cluster analysis using self-reported measures categorized students as better or poorer self-regulated learners; whereas a hierarchical cluster analysis using digital-trace measures clustered students as more active or less active online learners. One-way ANOVAs showed that: 1) better self-regulated learners had higher frequencies of interactions with three out of six online learning activities than poorer self-regulated learners. 2) More active online learners reported higher self-efficacy, higher intrinsic motivation, and more frequent use of positive self-regulated learning strategies, than less active online learners. Furthermore, a cross-tabulation showed significant (p < .01) but weak association between student clusters identified by self-reported and digital-trace measures, demonstrating self-reported and digital-trace descriptions of students’ self-regulated learning experiences were consistent to a limited extent. To help poorer self-regulated learners improve their learning experiences in blended course designs, teachers may invite better self-regulated learners to share how they approach learning in class. |
format | Online Article Text |
id | pubmed-10037358 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-100373582023-03-24 Self-reported and digital-trace measures of computer science students’ self-regulated learning in blended course designs Han, Feifei Ellis, Robert A. Educ Inf Technol (Dordr) Article This study investigated the extent to which self-report and digital-trace measures of students’ self-regulated learning in blended course designs align with each other amongst 145 first-year computer science students in a blended “computer systems” course. A self-reported Motivated Strategies for Learning Questionnaire was used to measure students’ self-efficacy, intrinsic motivation, test anxiety, and use of self-regulated learning strategies. Frequencies of interactions with six different online learning activities were digital-trace measures of students’ online learning interactions. Students’ course marks were used to represent their academic performance. SPSS 28 was used to analyse the data. A hierarchical cluster analysis using self-reported measures categorized students as better or poorer self-regulated learners; whereas a hierarchical cluster analysis using digital-trace measures clustered students as more active or less active online learners. One-way ANOVAs showed that: 1) better self-regulated learners had higher frequencies of interactions with three out of six online learning activities than poorer self-regulated learners. 2) More active online learners reported higher self-efficacy, higher intrinsic motivation, and more frequent use of positive self-regulated learning strategies, than less active online learners. Furthermore, a cross-tabulation showed significant (p < .01) but weak association between student clusters identified by self-reported and digital-trace measures, demonstrating self-reported and digital-trace descriptions of students’ self-regulated learning experiences were consistent to a limited extent. To help poorer self-regulated learners improve their learning experiences in blended course designs, teachers may invite better self-regulated learners to share how they approach learning in class. Springer US 2023-03-24 /pmc/articles/PMC10037358/ /pubmed/37361755 http://dx.doi.org/10.1007/s10639-023-11698-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Han, Feifei Ellis, Robert A. Self-reported and digital-trace measures of computer science students’ self-regulated learning in blended course designs |
title | Self-reported and digital-trace measures of computer science students’ self-regulated learning in blended course designs |
title_full | Self-reported and digital-trace measures of computer science students’ self-regulated learning in blended course designs |
title_fullStr | Self-reported and digital-trace measures of computer science students’ self-regulated learning in blended course designs |
title_full_unstemmed | Self-reported and digital-trace measures of computer science students’ self-regulated learning in blended course designs |
title_short | Self-reported and digital-trace measures of computer science students’ self-regulated learning in blended course designs |
title_sort | self-reported and digital-trace measures of computer science students’ self-regulated learning in blended course designs |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10037358/ https://www.ncbi.nlm.nih.gov/pubmed/37361755 http://dx.doi.org/10.1007/s10639-023-11698-5 |
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