Optimizing Formative Assessment with Learning Analytics

Formative assessment is a key element of effective education. Recently, a growing body of evidence has highlighted the potential of learning analytics to support formative assessment. However, this evidence is sparse, and concerns have been raised about the connection between learning analytics results and formative assessment models. When learning analytics results do not align well with formative assessment models, teachers may be hesitant to place trust in, interpret, and act upon the insights provided by learning analytics for formative assessment. In this study, we address this concern by adopting a well-established formative assessment model and conducting a systematic review to explore how learning analytics can support formative assessment. A total of 93 relevant articles, published between 2011 and 2023 and retrieved from Web of Science (WOS), Scopus, IEEE Xplore, and ACM, were analyzed using a coding scheme grounded in a formative assessment model. Drawing on the model of formative assessment, we report how learning analytics can support formative assessment by connecting the three key actors, teachers, students as peers, and students as self-assessors with the three critical stages of the process: (a) where the learner is going, (b) where the learner is now, and (c) how the learner is getting there. This study extends our understanding of the alignment between learning analytics results and theoretical concepts and further contributes to the advancement of the application of learning analytics for formative assessment.