Machine Learning as a Tool to Identify Critical Assignments

Authors

  • Jeffrey Heys Chemical and Biological Engineering Montana State University, Bozeman, MT

DOI:

https://doi.org/10.18260/2-1-370.660-106890

Abstract

  The application of Machine Learning (ML) tools to a wide range of problems from image recognition to movie recommendations is increasing rapidly. After a brief overview of ML, select ML tools are demonstrated through the analysis of student grades in various chemical engineering courses. ML tools are shown to help in the identification of important and particularly impactful assignments in various courses.

Accessibility Summary:

In accordance with Title II regulations this content meets all points of exemption as Archived web content and/or Preexisting conventional electronic documents.

Author Biography

Jeffrey Heys, Chemical and Biological Engineering Montana State University, Bozeman, MT

 

 Jeffrey J. Heys is a professor and department head in the Chemical and Biological Engineering department at Montana State University. He received his B.S. in chemical engineering in 1996 from Montana State University, and his M.S. and Ph.D. from the University of Colorado at Boulder in 1998 and 2001, respectively. His research area is computational transport and computational fluid dynamics in biological systems with an emphasis on fluid-structure interaction and porous media flows.

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Published

2018-09-17

Issue

Vol. 52 No. 4 (2018): Fall 2018

Section

Manuscripts