On learning platform metrics as markers for student success in a course

On learning platform metrics as markers for student success in a course
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学习平台指标作为学生课程成功的标志

DOI:
10.1002/cae.22653
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发表时间:
2023
影响因子:
2.9
通讯作者:
Clark, Renee
Clark, Renee
中科院分区:
工程技术4区
文献类型:
--
作者:
Yalcin, Ali;Kaw, Autar;Clark, Renee

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适应性学习平台越来越多地被用作各种教学模式的一部分。与本文特别相关的是,自适应学习是翻转课堂中个性化、课前学习的重要组成部分。以前无法访问的,由自适应学习平台生成的数据,关于学生参与课程内容提供了一个宝贵的机会,以获得更深入的了解学习过程,并改善后it.We的目的是调查自适应学习平台的互动和整体学生在课程中的成功之间的关系,并确定最有影响力的变量,学生的成功。我们对数值方法课程中收集的自适应学习平台数据进行了全面分析,包括汇总统计,频率分析和主成分分析,以确定哪些变量表现出最大的可变性,因此数据中的信息最多。随后,我们使用分区围绕中心聚类方法来调查自然发生的集群的学生,以及这些集群如何与整体表现的过程中。我们的研究结果表明,在课程中的整体表现,作为衡量的最终课程成绩,是强烈相关的(1)学生的行为互动与自适应平台和(2)他们的表现在自适应学习评估。我们还发现了不同的学生群体(根据课程的成功定义),表现出明显不同的行为。这些发现提供了定性和定量信息,以确定需要支持的学生,并为这些学生制定基于证据的支持策略。
Adaptive learning platforms are increasingly being used as part of varying instructional modalities. Particularly relevant to this paper, adaptive learning is a critical component of personalized, preclass learning in a flipped classroom. Previously inaccessible, data generated by adaptive learning platforms regarding student engagement with the course content provides an invaluable opportunity to gain a deeper understanding of the learning process and improve upon it. We aim to investigate the relationships between adaptive learning platform interactions and overall student success in the course and identify the variables most influential to student success. We present a comprehensive analysis of our adaptive learning platform data collected in a Numerical Methods course, including aggregate statistics, frequency analysis, and Principal Component Analysis, to determine which variables exhibited the most variability and, therefore, the most information in the data. Subsequently, we used the Partitioning Around Medoids clustering approach to investigate naturally occurring clusters of students and how these clusters relate to overall performance in the course. Our results show that overall performance in the course, as measured by the final course grade, is strongly associated with (1) the behavioral interactions of students with the adaptive platform and (2) their performance on the adaptive learning assessments. We also found distinct student clusters (as defined by success in the course) that exhibited distinctly different behaviors. These findings provide qualitative and quantitative information to identify students needing support and to craft an evidence‐based support strategy for these students.
DOI: --
发表时间: 2016
期刊: --
影响因子: --
作者:
S. Blau
通讯作者: S. Blau
DOI: --
发表时间: 2023
期刊: Proceedings of the 2022 ASEE Annual Conference and Exposition
影响因子: --
作者:
Kaw, A.
通讯作者: Kaw, A.
DOI: 10.18260/1-2--22585
发表时间: 2013-06
期刊: --
影响因子: --
作者:
Jacob Bishop;M. Verleger;Embry-Riddle Aeronautical;D. Beach
通讯作者: Jacob Bishop;M. Verleger;Embry-Riddle Aeronautical;D. Beach
DOI: --
发表时间: 2007
期刊:
影响因子: --
作者:
A. Kaw;M. Hess
通讯作者: M. Hess
DOI: 10.1080/02664763.2023.2220087
发表时间: 2023-06
影响因子: 1.5
作者:
Soumita Modak
通讯作者: Soumita Modak