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

Abstract

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. Vivekanandan KumarAthabasca University, Athabasca, Canada 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.

Questions in Smart Digital Environments

“…The potential of smart environments to provide new vistas for teaching and learning is already established within academic Freestone and Mason Questions in Smart Digital Environments discourse through journals specifically dedicated to this field (Spector, 2016;Saunders et al, 2017;Kumar, 2018;Martens et al, 2019;McKenna, 2019). The GGQs described in this paper might provide a means or a gateway through which some of this potential might be harnessed.…”

Education Smart Environments and Global e-Campus

“…Dynamic adaptive learning is educational learning, whereby the computer-based technologies are used to deliver resources and learning activities to the learners. The in-built technology that is part of the smart learning environments helps the individuals to collect information on the learners learning profiles (Kumar and Vivekanandan 2018). Importantly, the data on the learning profiles of the individuals should be collected over a period of time.…”

Visualization of Data Analysis for Evaluation of the Level of It-Specialists Competencies’ Creation

“…Big data and analytics in higher education have recently been seen as holding great potential to promote actions concerning 'administrative decision-making and organizational resource allocation', early identification of at-risk students and interventions to prevent them from failing, the development of more effective instructional techniques, and transforming the traditional view of the curriculum into a network of relations, using educational data collected regularly from learning management systems, social networks, learning activities, and the curriculum itself (Masino and Niño-Zarazúa, 2016;Braganza et al, 2017). The curriculum, its content and the learning outcomes are part of the educational data on the basis of which analytics can be used to study quality and higher education improvement and the level of graduates competence evaluation (Agasisti and Bowers, 2017;Kumar and Vivekanandan, 2018;Kulasegaram and Rangachari, 2018).…”