Bots and insights: Combining perspectives of analytics and software development in systems analysis and design projects

Madhav Sharma & Roger McHaney

Decision Sciences Journal of Innovative Education2025https://doi.org/10.1111/dsji.70005article
AJG 1ABDC C
Weight
0.37

What the paper says

Abstract Many management information systems (MIS) faculty have adopted a project‐oriented approach in their systems analysis and design courses. In these courses, students use a software development methodology to create a web or mobile application project, which can be based on a predefined case or developed for an external stakeholder. Because most information systems programs emphasize cybersecurity, analytics, and artificial intelligence (AI), traditional systems analysis and design courses for web applications may seem one‐dimensional in comparison. To address this limitation, we developed and implemented a project based on Merrill's Pebble‐in‐the‐Pond instructional design. In this project, students were required to build an application with two key components: a chatbot and a dashboard, both integrated with the same database. These components cater to multiple user groups, allowing us to combine perspectives from analytics and AI within a single project. Drawing from our experience and feedback from students, we have compiled a set of recommendations for successfully implementing such a project.

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https://doi.org/https://doi.org/10.1111/dsji.70005

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@article{madhav2025,
  title        = {{Bots and insights: Combining perspectives of analytics and software development in systems analysis and design projects}},
  author       = {Madhav Sharma & Roger McHaney},
  journal      = {Decision Sciences Journal of Innovative Education},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1111/dsji.70005},
}

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Evidence weight

0.37

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

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
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.