大数据分析将如何重塑电子学习?

Elena Șușnea
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引用次数: 1

摘要

在过去几年里,分析和数据挖掘已成为教育领域的新挑战,利用自动化方法的巨大优势来理解教育数据的想法得到了极大的推动。本文概述了学习分析和教育数据挖掘,并解释了如何利用它们来改善对教育工作者和学习者的支持,帮助他们努力取得高质量的学习成果(或结果),以及如何改善学术环境中的决策过程,以最大限度地提高战略成果。以数字化方式收集和存储的数据量巨大,而且种类繁多。因此,数据管理和分析科学也在不断进步,使教育领域能够将这一巨大资源转化为信息和知识,帮助教师和教育组织实现其目标。计算机科学家发明了大数据一词来描述这种不断发展的技术。大数据已成功应用于许多活动领域,尤其是电子学习领域。新的教育技术使学生、教育者和管理者能够通过移动设备、学习管理系统、社交网络、认知生物识别技术等产生大量数据,从而形成所谓的大数据。学习分析和教育数据挖掘技术可以从不同角度对大数据进行分析,包括监测学生的活动,设计教学干预措施以支持学生,预测学习的成功、失败和潜在辍学情况,调整/个性化学习内容和学习任务。有时,为了应用自动化方法分析教育环境,需要使用某种类型的个人数据,以帮助大学建立详细的学生档案,实现竞争优势。然而,学习分析和数据挖掘确实会对一些重要的道德价值观(如隐私和个性)构成威胁。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HOW BIG DATA ANALYTICS WILL RESHAPE E-LEARNING?
Over the last several years, analytics and data mining have become new challenges for educational field and the idea of using the great benefits of automated methods for making sense of educational data received a significant boost. This paper gives an overview about learning analytics and educational data mining and also explains the way they are used in order to improve the support for educators and learners in their strive to achieve high quality learning outcomes (or results) and the way they improve decision making process in academic environment to maximize strategic outcomes. The amount of data being digitally collected and stored is vast and heterogeneous. As a result, the science of data management and analysis is also advancing to enable educational field to convert this vast resource into information and knowledge that helps teachers and educational organizations to achieve their objectives. Computer scientists have invented the term big data to describe this evolving technology. Big data has been successfully used in the many areas of activity, and the e-learning in particular. New educational technologies have allowed students, educators and managers to generate tremendous data via mobile devices, learning management systems, social networks, cognitive biometrics technologies and so on, resulting in what has been called as big data. Learning analytics and educational data mining techniques allow big data to be analysed from different perspectives, including monitoring students' activities, designing pedagogical interventions to support students, prediction of the learning success, failure, and potential dropouts, adaptation/personalization of learning content and learning tasks. Sometimes, in order to apply automated methods to analyse an educational context, it is required the use some type of personal data that should help university to build detailed student profile, and achieve competitive advantage. However, learning analytics and data mining do pose a threat to some important ethical values like privacy and individuality.
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