Role of Educational Data Mining and Learning Analytics Techniques Used for Predictive Modeling

Kanksha Kaur, Omdev Dahiya
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Abstract

The use of data mining techniques to answer important educational questions is done with educational data mining that may be related to predicting students' performance or assessment. The purpose of these remains to address the goal of meeting and fulfilling the learning objectives. Learning analytics is a new approach for teachers to think about education and involves the visualization of data about students to improve learning. It is related to management strategies that prioritize quantitative measurements, which can sometimes be at odds with a teaching-focused approach to education. The purpose of this study is to perform a systematic literature review of the various techniques used in both educational data mining and learning analytics. The area of educational data mining utilizes data mining techniques to examine educational data. Meanwhile, learning analytics centers on utilizing data to gain knowledge about learning processes and student performance. The review will consider various techniques, including statistical methods, machine learning algorithms, and data visualization techniques, which are commonly used. The goal is to identify the most effective techniques for analyzing educational data and improving student learning outcomes. By performing a systematic literature review, this study will provide an overview of the current state of research in educational data mining and learning analytics. It will also identify gaps in the literature and suggest areas for future research. Ultimately, the findings of this study will be of great value to researchers, educators, and policymakers who are interested in using data to enhance the learning experience. For performing this, a few research questions have been designed to select relevant studies. In this study, the research articles from a decade, 2012 to 2022, are taken into consideration. From the overall search results, 41 studies have been taken into consideration that has addressed the scope and significance of this study.
用于预测建模的教育数据挖掘和学习分析技术的作用
使用数据挖掘技术来回答重要的教育问题是通过教育数据挖掘来完成的,这可能与预测学生的表现或评估有关。这些目标的目的仍然是解决满足和实现学习目标的目标。学习分析是教师思考教育的一种新方法,涉及到学生数据的可视化,以提高学习。它与优先考虑定量测量的管理策略有关,这有时可能与以教学为重点的教育方法不一致。本研究的目的是对教育数据挖掘和学习分析中使用的各种技术进行系统的文献综述。教育数据挖掘领域利用数据挖掘技术来检查教育数据。同时,学习分析的核心是利用数据来获得关于学习过程和学生表现的知识。本综述将考虑各种技术,包括常用的统计方法、机器学习算法和数据可视化技术。目标是确定最有效的技术来分析教育数据和提高学生的学习成果。通过进行系统的文献综述,本研究将概述教育数据挖掘和学习分析的研究现状。它还将确定文献中的空白,并提出未来研究的领域。最终,这项研究的结果将对那些有兴趣利用数据来提高学习体验的研究人员、教育工作者和政策制定者有很大的价值。为此,设计了一些研究问题来选择相关的研究。在本研究中,考虑了从2012年到2022年这十年的研究文章。从整体搜索结果来看,共考虑了41项研究,解决了本研究的范围和意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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