Abstract: We report on the design and development of an introductory analytics course delivered to almost 10,000 undergraduate business students to date. One novel aspect of the course is its orientation to add analytics capabilities to a business student’s toolbox, resulting in significant design and implementation implications. We anchored the course on three fundamental principles: maximizing learning, operating at scale, and a consistent experience for all learners. To enable a rigorous and valuable learning experience, the underlying course curriculum is based on the modified CRISP-DM (CRoss Industry Standard Process for Data Mining) framework. Bloom’s taxonomy is applied to the course assessments to evaluate the depth of learning. The course is delivered in a hybrid mode, arguably the best combination of online and face-to-face delivery modes. In a naturally occurring experimental setting, the COVID-19 pandemic accelerated the evolution of the course and generated additional reinforcing lessons. We explore those lessons and suggest directions for further research.

Keywords: Introductory analytics, Asynchronous learning, COVID-19, Bloom’s taxonomy, CRISP-DM

Download This Article: JISE2023v34n4pp360-369.pdf

Recommended Citation: Darcy, D. P., & Satpathy, A. (2023). Teaching Tip: A Scalable Hybrid Introductory Analytics Course. Journal of Information Systems Education, 34(4), 360-369.