Research in Learning Analytics and Educational Data Mining to Measure Self-Regulated Learning: A Systematic Review
World Conference on Mobile and Contextual Learning,
Up to date, most of the research to measure Self-Regulated Learning in students has primarily utilized self-report instruments. Recently, there has been a growing tendency towards using other assessment tools; particularly in the context of Learning Analytics and Educational Data Mining. However, there is a gap in the literature to review the application of new techniques used in these domains related to data analytics. To address this gap, we conducted a systematic literature review focusing on the measurement of Self-Regulated Learning features and behaviours in students based on the analysis of tracking and log data using techniques such as cluster analysis, regression or classification; either solely, or associated with self-report instruments. This review aims to categorize the data used in the different papers to measure Self-Regulated Learning and to recognize the behaviours/features/components measured. In addition, it also analyses the most frequently used tools and the application disciplines. This systematic literature review surveys the literature for an eight-year time span from 2011 to 2019, following the general guidelines of systematic reviews with clearly established eligibility criteria. After applying the eligibility criteria, 109 studies were identified as relevant. The findings show an increasing interest in the use of Learning Analytics and Educational Data Mining for assessing Self-Regulated Learning in students, and the tendency to associate the new data analysis techniques with other self-reported measures to obtain data triangulation.
ElSayed, A.A., Caeiro-Rodríguez, M., MikicFonte, F.A. & Llamas-Nistal, M. (2019). Research in Learning Analytics and Educational Data Mining to Measure Self-Regulated Learning: A Systematic Review. In Proceedings of World Conference on Mobile and Contextual Learning 2019 (pp. 46-53).