数据分析、网络知识结构与学习成绩

IF 1.6 Q3 BUSINESS, FINANCE Journal of Emerging Technologies in Accounting Pub Date : 2023-10-01 DOI:10.2308/jeta-2022-056
Freddie Choo, Kim Tan
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摘要

摘要本研究的第一个目的是探讨数据分析是否可以形成一个网状的知识结构(NKS)的学习课程材料在会计。我们测试了一组使用数据分析来解决资产盗用案例研究的学生和一组没有使用数据分析的对照组。我们发现数据分析已经形成了这样一个结构的证据。第二个目的是调查NKS是否与学习成绩有关。我们对nks和考试成绩进行了回归分析。我们发现证据表明,高连接和处理效率的NKS与更好的会计考试成绩相关。总体而言,研究结果表明,将数据分析整合到会计课程中,通过形成与学习成绩正相关的NKS,有利于课程材料的学习。本研究做出了一些贡献,包括将主要在认知科学领域进行的NKS工作扩展到会计领域。
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Data Analytics, Netlike Knowledge Structure, and Academic Performance
ABSTRACT The first objective of this study was to investigate whether data analytics could form a netlike knowledge structure (NKS) of learned course materials in accounting. We tested a group of students that used data analytics to solve an asset misappropriation case study and a control group that did not. We found evidence that data analytics has formed such a structure. The second objective was to investigate whether NKS was associated with academic performance. We conducted regression analyses on the NKSs and test scores. We found evidence that NKS with high connectivity and processing efficiency was associated with better accounting test scores. Overall, the findings imply that integrating data analytics into accounting courses benefits the learning of course materials by forming an NKS positively associated with academic performance. This study makes several contributions, including extending the work on NKS conducted predominantly in the cognitive science domain to the accounting domain.
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来源期刊
CiteScore
4.30
自引率
27.80%
发文量
14
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