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Scoreboard for Excel: The Design and Implementation of Software to Advance Active Learning, Automate Grading, and Reduce Cheating

Mary B. Sasmaz et al.

Issues in Accounting Education2025https://doi.org/10.2308/issues-2023-044article
AJG 2ABDC A
Weight
0.48

Abstract

This study discusses the design and implementation of software to advance active learning, automate grading, and reduce cheating. The software, Scoreboard for Excel, enables faculty to author assignments that provide real-time formative feedback efficiently, even in large class sizes. Scoreboard automatically converts existing Excel workbooks into real-time self-grading assignments. Scoreboard assignments provide real-time formative feedback so students can fix their mistakes and learn from them. Additionally, it combats cheating by creating unique versions of the workbook for each student. The system was designed using the design science research methodology. Following its introduction, we will provide the outcomes of an Institutional Review Board (IRB)-approved classroom experiment conducted to test the efficacy of Scoreboard. Our analysis shows that it positively affects student performance and experience. Scoreboard leverages Excel to improve education in beginning or advanced accounting curricula. Scoreboard is free for noncommercial educational purposes. JEL Classifications: A22; M49; I21; I29.

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https://doi.org/https://doi.org/10.2308/issues-2023-044

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@article{mary2025,
  title        = {{Scoreboard for Excel: The Design and Implementation of Software to Advance Active Learning, Automate Grading, and Reduce Cheating}},
  author       = {Mary B. Sasmaz et al.},
  journal      = {Issues in Accounting Education},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.2308/issues-2023-044},
}

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Scoreboard for Excel: The Design and Implementation of Software to Advance Active Learning, Automate Grading, and Reduce Cheating

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Evidence weight

0.48

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

F · citation impact0.41 × 0.4 = 0.16
M · momentum0.63 × 0.15 = 0.09
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.