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Demystifying Learning Analytics in Personalised Learning

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TLDR
The paper proposes that learning analytics is dependent on personalised approach for both educators and students, and defines the characterising features that represents the relationship between learning analytics and personalised learning environment.
Abstract
This paper presents learning analytics as a mean to improve students’ learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper’s contribution to knowledge is in consider ing personalised learning within the context framework of learning analytics

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Journal ArticleDOI

Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment

TL;DR: Examination of the indirect relationship between a big data analytics capability (BDAC) and two types of innovation capabilities finds that dynamic capabilities fully mediate the effect on both incremental and radical innovation capabilities.
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Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies

TL;DR: The results show that LA especially are integrated into the current business models of EdTech companies on three levels, which are as follows: basic Learning Analytics, Learning Analytics and algorithmic or human-based recommendations, and Learning analytics and adaptive teaching and learning (AI based).
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Higher Education’s Microcredentialing Craze: a Postdigital-Deweyan Critique

TL;DR: In this article, the authors argue that the craze for microcredentialing reflects administrative urgency to unbundle higher education curricula and degree programs for greater efficiency and profitability and a renascent movement among industry and higher education leaders to reorient the university curriculum towards vocational training.
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Integration of Data Mining Clustering Approach in the Personalized E-Learning System

TL;DR: A clustering approach to partition students into different groups or clusters based on their learning behavior is presented and it is observed that more robust results can be achieved by the replacement of existing methods with CFSFDP-HD.
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Empowering corporate social responsibility (CSR): insights from service learning

TL;DR: In this article, the authors explored service learning with its insights in empowering corporate responsibility awareness, which could enlarge creative thinking with envisioning sustainability and corporate responsibility, which can enhance the understanding stage about the care for protecting the environmental concern within learning experience with the goal to produce responsible awareness especially by economic agents such as shareholders, managers, regulators and active participants.
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Journal Article

Connectivism: A Learning Theory for the Digital Age

TL;DR: In this paper, the authors present an approach for teaching distance learning in Instructional Technology and Distance Learning (ITDL) courses, based on the International Journal of Instructional technology and distance learning (IITDL).
Proceedings ArticleDOI

The State of Educational Data Mining in 2009: A Review and Future Visions

TL;DR: This paper reviewed the history and current trends in the field of EDM and discussed trends and shifts in the research conducted by this community, and discussed the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries, and the reduction in the frequency of relationship mining within the EDM community.
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Learning analytics: drivers, developments and challenges

TL;DR: This review of the field begins with an examination of the technological, educational and political factors that have driven the development of analytics in educational settings, and goes on to chart the emergence of learning analytics.
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Transforming American Education: Learning Powered by Technology

TL;DR: In this article, the authors present a special issue of E-Learning and Digital Media devoted to a discussion of the National Education Technology Plan, Transforming American Education: Learning Powered by Technology, presented as a means to leverage the innovation and ingenuity required to improve student learning and to accelerate the adoption and use of effective technology practices in America's schools.
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