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

Kinshuk Kumar,Vivekanandan Vivekanandan

doi:10.1108/aaouj-12-2017-0039

Kinshuk Kumar, Vivekanandan Vivekanandan

Open Access

https://doi.org/10.1108/aaouj-12-2017-0039

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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.

Highlights

Since its emergence, learning analytics (LA) has continued to prove its importance to the field of education
Self-/Co-regulated learning (SCRL) works with different types of learning sensors, such as MI-WRITER and CODing experience (CODEX) Smart learning for data collection, and it provides a dashboard to visualize student proficiency levels in various domains
The “smartness” of Smart LA depends on how adaptive the system is, and how well it is able to sense, infer, anticipate and promote self-learning/-organization (Uskov et al, 2017, p. 194)

Summary

Introduction

Since its emergence, learning analytics (LA) has continued to prove its importance to the field of education. SCALE is a smart analytics technology that measures student competence by translating learning traces from data collected from different learning sensors such as CODEX. SCRL works with different types of learning sensors, such as MI-WRITER and CODEX Smart learning for data collection, and it provides a dashboard to visualize student proficiency levels in various domains. Learners can learn at their own pace, and teachers can monitor student progress, adapting feedbacks and assistance to relevant big data from the respective smart learning environments. Lambda is an LA platform that centralizes, senses, analyses and demonstrates analytics data from different software tools It is a competence management system measuring both learner proficiency and confidence levels (Seanosky et al, 2016). Their findings showed that the new design was likely to be effective in optimizing student learning experiences; the system adoption time might make the process longer

Conclusion