SCALE: A Smart Competence Analytics Solution for English Writing

Boulanger, David; Seanosky, Jeremie; Clemens, Clayton; Kumar, Vive; Kinshuk, Kinshuk

doi:10.1109/icalt.2016.108

Cited by 3 publications

(1 citation statement)

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“…It presents the results of analyses carried out by Smart Competence Analytics on LEarning (SCALE) engine (Boulanger, Seanosky, Clemens, Kumar and Kinshuk, 2016;Boulanger, Seanosky, Pinnell, Bell, Kumar and Kinshuk, 2016). SCALE is a smart analytics technology that measures student competence by translating learning traces from data collected from different learning sensors such as CODEX.…”

Section: Mixed-initiative Dashboard (Mi-dash)mentioning

confidence: 99%

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

Kinshuk

Kumar

2018

AAOUJ

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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.

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Section: Mixed-initiative Dashboard (Mi-dash)mentioning

confidence: 99%