Using learning design and learning analytics to promote, detect and support Socially-Shared Regulation of Learning: A systematic literature review

Recent developments in educational technology research underscores the importance of individuals and groups to regulate their own learning processes and behaviours to cope with the fast-changing world around them. This led many researchers to focus on the concept of Socially-Shared Regulation of Lea...

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Published in:Computers and education Vol. 232; p. 105261
Main Authors: Villa-Torrano, Cristina, Suraworachet, Wannapon, Gómez-Sánchez, Eduardo, Asensio-Pérez, Juan I., Bote-Lorenzo, Miguel L., Martínez-Monés, Alejandra, Zhou, Qi, Cukurova, Mutlu, Dimitriadis, Yannis
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.07.2025
Subjects:
Collaborative learning
Learning analytics
Learning design
Socially shared regulation of learning
Learning analytics
Learning design
Collaborative learning
Socially shared regulation of learning
ISSN:0360-1315
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Summary:Recent developments in educational technology research underscores the importance of individuals and groups to regulate their own learning processes and behaviours to cope with the fast-changing world around them. This led many researchers to focus on the concept of Socially-Shared Regulation of Learning (SSRL) which tries to understand the different types of collective regulatory processes that emerge while learning in groups. Although initial investigations have predominantly theorised these phenomena, there is a growing need to operationalise SSRL to prepare learners for a future in which regulation of their learning is a key skill for success. This necessitates systematic examination of how Learning Design (LD) and Learning Analytics (LA) can be leveraged to promote, detect, and support SSRL. Therefore, this paper presents a systematic literature review of 110 empirical studies with the aim of identifying: (i) what does empirical literature consider as SSRL; (ii) how is LD used to promote SSRL; (iii) how are LA and LD used to detect SSRL; and (iv) how are LD and LA used to support SSRL. The findings from the literature indicate three major challenges to the operationalisation of SSRL support in the real-world: (i) the lack of convergence in theoretical models, together with the lack of validated instruments for detecting (e.g., coding schemes) and measuring (e.g., questionnaires) SSRL processes; (ii) the types of data most frequently collected and the analysis techniques used make it difficult to provide SSRL support to the students during the learning situations; and (iii) there is a lack of tools designed to promote, detect, and support SSRL processes. This paper describes each challenge, and provides a discussion about potential future research opportunities for tackling them. •There is a growing, but still incomplete, convergence towards unified SSRL theoretical models.•Validated instruments for detecting and measuring SSRL processes are still needed.•Purposeful adaptation of the learning design contributes to the promotion of SSRL.•Typical data collection and analysis applied do not allow to support SSRL processes.•There is a lack of tools aimed at promoting, detecting and supporting SSRL processes.
ISSN:0360-1315
DOI:10.1016/j.compedu.2025.105261