电子学习中学生评价的数据挖掘研究

Wenty Dwi Yuniarti, E. Winarko, Aina Musdholifah
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引用次数: 1

摘要

技术在学习中的传播越来越广泛,其标志是学习迅速转移到在线环境,如电子学习。评估是教育的重要组成部分。电子学习中的评估要求方法高效有效。数据挖掘是一种揭示和识别教育数据库中隐藏模式的分析方法。在e-Learning评估中深化数据挖掘是一个有趣的问题,同时也是一个挑战,教师和机构需要找到正确的方法并在这一领域做出重大贡献。因此,我们进行了文献综述,并从2016年至2020年发表的相关文献中提出了最新的电子学习学生评估数据挖掘。我们特别关注e-Learning中的学生评估研究,即利用数据挖掘的范围,几种方法的比较,以及与评估相关的几个方面的分析。本研究也为今后的研究方向指明了方向。我们将过程挖掘方法确定为当前趋势评估的数据挖掘子学科。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Mining for Student Assessment in e-Leaming: A Survey
The diffusion of technology in learning is increasingly massive, marked by the rapid transfer of learning into online environments such as e-Learning. Assessment is an important element of education. Assessment in e-Learning requires methods to be efficient and effective. Data mining is a method of analysis to reveal and recognize hidden patterns in educational databases. Deepening data mining for assessment in e-Learning is both an interesting and a challenge for teachers and institutions to find the right method and make a significant contribution in this area. Therefore, we conducted a literature review and presented state-of-the-art data mining for student assessment in e-Learning from relevant literature publishing from 2016 to 2020. We specifically focus on student assessment research in e-Learning, namely the scope of utilizing data mining, a comparison of several methods, and an analysis of several aspects related to assessment. This study also sheds light on future research directions. We identify the process mining approach as a data mining sub-discipline for the current trend assessment.
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