Description
Many data analytics classes utilize canned data, already cleansed, and with simplified scenarios for students to perform analysis. To better train students to tackle real-world analytics problems, using big data from a real company builds on prior research on the effectiveness of experiential learning. In this study, we introduced experiential learning into a data analytics class by working on an analytics project with a large Fortune 500 company. Our live case allowed students the opportunity to see firsthand the complex nature of analytics being more than just number crunching. Based on a student reflective assignment, we found that student learning outcomes and motivation were greatly enhanced. However, there were numerous challenges presented through the course of the study, including unclear expectations, ambiguity of the research project, and data quality issues. Our suggestions are that aligning expectations, effective communications, and managing and encouraging failure can be drivers of success.
Recommended Citation
Goh, Samuel and Zhang, Xiaoni, "Incorporating Experiential Learning into Big Data Analytic Classes" (2015). AMCIS 2015 Proceedings. 24.
https://aisel.aisnet.org/amcis2015/ISEdu/GeneralPresentations/24
Incorporating Experiential Learning into Big Data Analytic Classes
Many data analytics classes utilize canned data, already cleansed, and with simplified scenarios for students to perform analysis. To better train students to tackle real-world analytics problems, using big data from a real company builds on prior research on the effectiveness of experiential learning. In this study, we introduced experiential learning into a data analytics class by working on an analytics project with a large Fortune 500 company. Our live case allowed students the opportunity to see firsthand the complex nature of analytics being more than just number crunching. Based on a student reflective assignment, we found that student learning outcomes and motivation were greatly enhanced. However, there were numerous challenges presented through the course of the study, including unclear expectations, ambiguity of the research project, and data quality issues. Our suggestions are that aligning expectations, effective communications, and managing and encouraging failure can be drivers of success.