Enhancing Student Success by Combining Pre-enrollment Risk Prediction with Academic Analytics Data
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Since 1905, the Department of Agricultural Engineering, now the Department of Agricultural and Biosystems Engineering (ABE), has been a leader in providing engineering solutions to agricultural problems in the United States and the world. The department’s original mission was to mechanize agriculture. That mission has evolved to encompass a global view of the entire food production system–the wise management of natural resources in the production, processing, storage, handling, and use of food fiber and other biological products.
History
In 1905 Agricultural Engineering was recognized as a subdivision of the Department of Agronomy, and in 1907 it was recognized as a unique department. It was renamed the Department of Agricultural and Biosystems Engineering in 1990. The department merged with the Department of Industrial Education and Technology in 2004.
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1905–present
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- Department of Agricultural Engineering (1907–1990)
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- College of Agriculture and Life Sciences (parent college)
- College of Engineering (parent college)
- Department of Industrial Education and Technology, (merged, 2004)
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Abstract
For nearly a decade, our institution has used multiple-linear-regressions models to predict student success campus-wide. Over the past three years, we worked to refine the success prediction models to the college of engineering (COE) students in particular, and to explore the use of classification and regression tree (CART) approaches to doing the prediction (e.g., Authors, 2016). In a parallel effort, our institution has contracted with an academic analytics company to do a retrospective analysis of student performance in every course as the university in relation to graduation rates. Here, we report on recent work we have done to make synergistic use of the results from the COE CART model and the academic analytics. Specifically, we have been able to examine student performance (i.e., grades) in core “success marker” courses as a function of the risk-grouping into which the CART model places them. We are now using this information to inform our advising. We provide details on these efforts, and on the opportunities and challenges provided by data-driven approaches to enhancing student success.
Comments
This proceeding is published as Raman, D. Raj, and Amy L. Kaleita. "Enhancing student success by combining pre-enrollment risk prediction with academic analytics data." Paper ID #18536. In 2017 ASEE Annual Conference & Exposition. 2017. DOI: 10.18260/1-2--28281. Posted with permission.