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Open Access Journal Article

Learning Analytics and Educational Data Mining

by Sophia White 1,*
1
Sophia White
*
Author to whom correspondence should be addressed.
PIE  2022 4(2):31; https://doi.org/10.69610/j.pie.20220822
Received: 17 June 2022 / Accepted: 20 July 2022 / Published Online: 22 August 2022
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Abstract

The rapid advancement of technology in education has led to an unprecedented amount of data being generated from various educational systems. This abundance of data has opened new avenues for the field of learning analytics (LA) and educational data mining (EDM). This paper explores the intersection of these two disciplines, highlighting their significance in understanding student performance, learning patterns, and educational outcomes. Learning analytics involves the application of statistical analysis, data mining techniques, and machine learning algorithms to analyze educational data for insights that can inform educational decision-making. Educational data mining, on the other hand, focuses on discovering useful patterns and knowledge from large educational datasets. The paper discusses the benefits and challenges of these approaches, including issues related to data privacy and ethical considerations. Additionally, it examines how LA and EDM can be used to personalize learning experiences, improve curriculum design, and support educational administrators in making evidence-based decisions. The paper concludes with a call for further research and development in this domain to harness the potential of learning analytics and educational data mining for enhancing the quality and effectiveness of education.

Keywords: Learning Analytics; Educational Data Mining; Educational Outcomes; Data Privacy; Personalized Learning.;
Copyright: © 2022 by White. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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APA Style
White, S. (2022). Learning Analytics and Educational Data Mining. Perspectives in Innovative Education, 4(2), 31. doi:10.69610/j.pie.20220822
ACS Style
White, S. Learning Analytics and Educational Data Mining. Perspectives in Innovative Education, 2022, 4, 31. doi:10.69610/j.pie.20220822
AMA Style
White S. Learning Analytics and Educational Data Mining. Perspectives in Innovative Education; 2022, 4(2):31. doi:10.69610/j.pie.20220822
Chicago/Turabian Style
White, Sophia 2022. "Learning Analytics and Educational Data Mining" Perspectives in Innovative Education 4, no.2:31. doi:10.69610/j.pie.20220822

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ACS Style
White, S. Learning Analytics and Educational Data Mining. Perspectives in Innovative Education, 2022, 4, 31. doi:10.69610/j.pie.20220822
AMA Style
White S. Learning Analytics and Educational Data Mining. Perspectives in Innovative Education; 2022, 4(2):31. doi:10.69610/j.pie.20220822
Chicago/Turabian Style
White, Sophia 2022. "Learning Analytics and Educational Data Mining" Perspectives in Innovative Education 4, no.2:31. doi:10.69610/j.pie.20220822
APA style
White, S. (2022). Learning Analytics and Educational Data Mining. Perspectives in Innovative Education, 4(2), 31. doi:10.69610/j.pie.20220822

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