克服教育数据浪费的方法:系统回顾

Q4 Multidisciplinary
Aminah Aldossary, L. Alfarani
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引用次数: 0

摘要

本研究旨在通过强调最近的研究趋势和回顾智能技术在该领域的最佳实践,对最近关于教育数据挖掘主题的科学研究进行系统回顾。目前的研究仅限于2020年至2022年10月期间通过IEEE数据库发表的英文科学研究和会议。在应用PRISMA表格并审查和记录方法后,确定了25篇符合标准的论文。目前的研究结果表明,预测趋势是教育数据挖掘领域最常见的研究趋势之一。此外,这一趋势在教育科学方面的覆盖范围也有显著差异。另一方面,结果表明,为学习者推荐最合适的专业课程的趋势是最不常见的研究趋势之一。在智能技术实践方面,研究结果表明,在实际框架和算法中使用最多和最成熟的智能技术是用于预测目的的,这些技术被证明可以极大地增强研究人员获得适用于实际教育环境的准确和合乎逻辑的结果的能力。关键词:人工智能,机器学习,算法,神经网络,教育数据,教育机构
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Methods of Overcoming Data Waste in Education: A Systematic Review
This research aimed to perform a systematic review of recent scientific studies that addressed the topic of educational data mining by highlighting recent research trends and reviewing the best practices of smart technology in this field. The current research was restricted to scientific studies and conferences in English that were published through the IEEE database between 2020 and October 2022. After applying the PRISMA form and reviewing and documenting the method, 25 papers were identified that matched the criteria. The outcomes of the current research concluded that the trend of prediction was one of the most common research trends in the domain of educational data mining. Moreover, this trend varied remarkably in its coverage of educational scientific aspects. On the other hand, the outcomes demonstrated that the trend of recommending the most appropriate specialised tracks for learners was one of the least common research trends. With regard to intelligent technical practices, the outcomes of the research revealed that the most used and mature intelligent technologies in practical frameworks and algorithms were intended for prediction purposes, and these were shown to greatly enhance researchers’ ability to reach accurate and logical results that are applicable in real educational contexts. KEYWORDS Artificial intelligence, machine learning, algorithms, neural networks, educational data, educational institutions
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来源期刊
Scientific Journal of King Faisal University
Scientific Journal of King Faisal University Multidisciplinary-Multidisciplinary
CiteScore
0.60
自引率
0.00%
发文量
0
期刊介绍: The scientific Journal of King Faisal University is a biannual refereed scientific journal issued under the guidance of the University Scientific Council. The journal also publishes special and supplementary issues when needed. The first volume was published on 1420H-2000G. The journal publishes two separate issues: Humanities and Management Sciences issue, classified in the Arab Impact Factor index, and Basic and Applied Sciences issue, on June and December, and indexed in (C​ABI) and (SCOPUS) international databases.
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