Preparing Accounting Students for Careers Utilizing Data Analytics: Curriculum Innovation Steps and Challenges

Kevin Pan & Matthew J. Mazzei

AIS Educator Journal2023https://doi.org/10.3194/1935-8156-18.1.1article
ABDC C
Weight
0.41

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Abstract Data analytics has become increasingly important in the accounting profession. To prepare accounting students for careers that require data analytic skills, accounting departments in institutions of higher education need to integrate data analytics into existing curricula. While the curriculum development process has been described extensively in prior literature, adding data analytics to accounting curriculum proves challenging for several reasons, including the complexity of the subject of data analytics, insufficient faculty resources, and the limited credit hours accounting students have available for new courses. In this paper, we describe our business school’s seven-step process of incorporating data analytics into the accounting curriculum. Since analytics learning objectives cannot be taught within the accounting curriculum by accounting faculty alone, this curriculum innovation required collaboration with other faculty. This analytics curriculum update required buy-in from various stakeholders, all done despite notoriously limited time and resources. We were able to see accounting students succeed in their internships and careers using analytics learned from this new curriculum. In addition to describing the steps, we discuss the challenges faced and lessons learned from integrating accounting and analytics education.

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https://doi.org/https://doi.org/10.3194/1935-8156-18.1.1

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@article{kevin2023,
  title        = {{Preparing Accounting Students for Careers Utilizing Data Analytics: Curriculum Innovation Steps and Challenges}},
  author       = {Kevin Pan & Matthew J. Mazzei},
  journal      = {AIS Educator Journal},
  year         = {2023},
  doi          = {https://doi.org/https://doi.org/10.3194/1935-8156-18.1.1},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.17 × 0.4 = 0.07
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.