Personalized Support Features Learners Expect From Self-Regulated Learning Analytics
Dwiarie, Adinda; Nguyen, Andy; Lämsä, Joni; Järvelä, Sanna (2023-09-29)
IEEE
29.09.2023
A. Dwiarie, A. Nguyen, J. Lämsä and S. Järvelä, "Personalized Support Features Learners Expect From Self-Regulated Learning Analytics," 2023 IEEE International Conference on Advanced Learning Technologies (ICALT), Orem, UT, USA, 2023, pp. 66-67, doi: 10.1109/ICALT58122.2023.00025
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202502061492
https://urn.fi/URN:NBN:fi:oulu-202502061492
Tiivistelmä
Abstract
Self-regulated learning (SRL) is a critical skill for lifelong learning. However, many learners struggle with SRL and need support. With the recent advancement, learning analytics (LA) has offered capabilities for supporting learning, particularly, for SRL in lifelong learning. In designing SRL analytics, recent calls asked to consider the learners' voices as well as to apply the learning theories. This study aims to explore learners' expectations of personalized support features from SRL analytics based on the learners' challenges and needs in the different phases of SRL. We conducted 10 focus group discussions with 27 students from non-formal online professional development courses. We applied thematic analysis to explore the challenges faced by learners and their expectations of SRL analytics features to support their needs. The findings highlight the importance of features to personalize goal setting, progress tracking, socio-emotional and motivational support, and feedback among other features, in facilitating SRL. The results of the study provide insights into the design of SRL analytics that can effectively support learners.
Self-regulated learning (SRL) is a critical skill for lifelong learning. However, many learners struggle with SRL and need support. With the recent advancement, learning analytics (LA) has offered capabilities for supporting learning, particularly, for SRL in lifelong learning. In designing SRL analytics, recent calls asked to consider the learners' voices as well as to apply the learning theories. This study aims to explore learners' expectations of personalized support features from SRL analytics based on the learners' challenges and needs in the different phases of SRL. We conducted 10 focus group discussions with 27 students from non-formal online professional development courses. We applied thematic analysis to explore the challenges faced by learners and their expectations of SRL analytics features to support their needs. The findings highlight the importance of features to personalize goal setting, progress tracking, socio-emotional and motivational support, and feedback among other features, in facilitating SRL. The results of the study provide insights into the design of SRL analytics that can effectively support learners.
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