Advancing learning through smart learning analytics: a review of case studies

Abstract

Purpose Smart learning analytics (Smart LA) – i.e. the process of collecting, analyzing and interpreting data on how students learn – has great potentials to support opportunistic learning and offer better – and more personalized – learning experiences. The purpose of this paper is to provide an overview of the latest developments and features of Smart LA by reviewing relevant cases. Design/methodology/approach The paper studies several representative cases of Smart LA implementation, and highlights the key features of Smart LA. In addition, it discusses how instructors can use Smart LA to better understand the efforts their students make, and to improve learning experiences. Findings Ongoing research in Smart LA involves testing across various learning domains, learning sensors and LA platforms. Through the collection, analysis and visualization of learner data and performance, instructors and learners gain more accurate understandings of individual learning behavior and ways to effectively address learner needs. As a result, students can make better decisions when refining their study plans (either by themselves or in collaboration with others), and instructors obtain a convenient monitor of student progress. In summary, Smart LA promotes self-regulated and/or co-regulated learning by discovering opportunities for remediation, and by prescribing materials and pedagogy for remedial instruction. Originality/value Characteristically, Smart LA helps instructors give students effective and efficient learning experiences, by integrating the advanced learning analytics technology, fine-grained domain knowledge and locale-based information. This paper discusses notable cases illustrating the potential of Smart LA.