K-12教育的数据科学

Julie L. Harvey, S. Kumar
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引用次数: 5

摘要

数据科学是一个可以在各种环境中使用的领域。教育是正在扩大使用数据科学来提高教育质量的领域之一。美国将小学和中学分为幼儿园(K)到12年级。这是学院/大学之前教育水平的代表。K-12教育中的数据科学是被评估的,对教育领域很重要,因为教育者、管理者和利益相关者总是在寻找缩小成就差距和提高所有学生表现的方法。使用数据科学对学生的表现进行评估对于缩小这一差距至关重要。数据挖掘用于学生成绩、教育计划和教育指导的评估和分析。它也被用来创建未来学生成功的预测模型。K-12教育数据集将用于评估学生的表现。本文将根据各种因素和数据来探索和展示学生的表现。讨论了K-12教育中的数据科学及其对学生表现和教育者使用这些数据的影响。我们还对现有的K-12教育数据分析工作及其局限性进行了审查。
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Data Science for K-12 Education
Data science is a field that can be used in a variety of settings. Education is one of the fields that is expanding its use of data science to improve the quality of education. The United States denotes primary and secondary school as grades kindergarten (K) through 12th grade. This is representative of education prior to college/university level. Data science in K-12 education is evaluated and important to the field of education because educators, administrators, and stakeholders are always looking for ways to close the achievement gap and increase performance of all students. Student performance evaluation using data science is crucial to closing this gap. Data mining is used in the evaluation and analysis of student performance, educational programs and educational instruction. It is also used to create prediction models for future student success. A K-12 education dataset will be used to evaluate student performance. This paper will explore and display student performance based on a variety of factors and data. Data science in K-12 education and its impact on student performance and educator use of this data is discussed. We have also performed review of existing work in the data analytics for K-12 education along with their limitations.
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