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

Abstract

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 operationalize 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 operationalization 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.