A learning analytics case study: On class sizes in undergraduate writing courses

A learning analytics case study: On class sizes in undergraduate writing courses
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学习分析案例研究:本科写作课程的班级规模

DOI:
10.1002/sta4.527
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发表时间:
2023
期刊:
影响因子:
1.7
通讯作者:
Bresciani Ludvick, Marilee J.
Bresciani Ludvick, Marilee J.
中科院分区:
数学4区
文献类型:
--
作者:
Levine, Richard A.;Rivera, Patricia E.;He, Lingjun;Fan, Juanjuan;Bresciani Ludvick, Marilee J.

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随着学生学习成绩和学术背景数据的收集和可用性,高等教育机构最近加强了学习分析的举措和基础设施,利用大量数据为学生的成功提供信息。随着学生成功的定义不同,从什么预测特定的职业准备能力的水平,以完成学位的分析,环境是一个肥沃的土壤统计实践和合作之间的统计精明,但不同的客户讲师,方案顾问和管理人员。在本文中,我们讨论了我们的经验,为此,通过一个咨询项目的影响,写作课程班级规模对学生实现毕业写作要求的评估。在详细介绍该项目的工作流程和挑战时,我们分享了统计沟通和报告的各个方面、我们的研究小组开发的用于处理混杂因素的创新统计方法的应用以及通过跨学科应用机构研究专业发展计划进行的相关输入和培训。本文说明了如何通过这些组成部分中的每一个灌输对统计推断的欣赏,对于在一般统计实践中捕获机构购买数据知情决策是非常宝贵的。
With the collection and availability of data on student academic performance and academic background, higher education institutions have recently stepped up initiatives in and infrastructure for learning analytics, leveraging this deluge of data to inform student success. With definitions of student success varying from analyses of what predicts levels of specific career readiness competencies to degree completion, the environment is a fertile ground for statistical practice and collaboration among a statistically savvy yet diverse clientele of instructors, programme advisors and administrators. In this paper, we discuss our experiences to this end through a consulting project evaluating the impact of writing course class size on students achieving a graduation writing requirement. In detailing the workflow for and challenges in this project, we share aspects of statistical communication and reporting, applications of innovative statistical methodology developed by our research group for handling confounding factors and correlated inputs and training through an interdisciplinary applied institutional research professional development programme. This paper illustrates how instilling an appreciation for statistical inference through each of these components is invaluable for capturing institutional buy‐in for data‐informed decision‐making in general statistical practice.
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