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In the era of big data where data is being embraced by academic institutions, each academic department has access to lots of data –enrollment data, retention data, student outcomes, faculty productivity, student success rates and resource allocation. As a large four-year public institution, our institution serves a diverse student body where more than 60% of students are considered as economic disadvantaged. In our department (comprising 1900 students and 120 faculty), we are currently using data-driven decision-making to gain deeper insights into the needs of students, faculty and staff. Such well-planned and implemented data-driven strategy has transformed those insights into student success – retention and enrollment. Another area that data-driven culture has benefitted is in creating an unbiased environment (between faculty-student, administration-faculty, and faculty-faculty) where collaboration and communication has become easier.

The main objective of this paper is to present our three data-analytic tools: predictive, descriptive and prescriptive and how they have improved student outcomes, intervened at-risk students, strategized cost cutting in the department, project actual outcomes and finally determining the effectiveness of our data-decisions. For example, our Predictive tool is helping identify potential low performing students at the course level and assigning them to mentoring and tutoring resources. Our Prescriptive tool is helping with strategies for cost-cutting suggestions and improving retention at the department level. Our Descriptive tool is helping with data-driven unbiased communication between staff, faculty and students at the college level.


Satyanarayana, A. (2023, March), Data Analytics for Decision Making at Academic Departments Paper presented at ASEE Zone 1 Conference - Spring 2023, State College,, Pennsylvania. © 2023 American Society for Engineering Education.



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