Regression Analysis to Predict Student Electric Circuits Performance | Zendy

Matthew Young | Zendy; Edward Carl Greco | Zendy; Scott M. Jordan | Zendy; Thomas Limperis | Zendy

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Regression Analysis to Predict Student Electric Circuits Performance

Author(s) -

Matthew Young,

Edward Carl Greco,

Scott M. Jordan,

Thomas Limperis

Publication year - 2020

Publication title -

2019 asee annual conference and exposition proceedings

Language(s) - English

Resource type - Conference proceedings

DOI - 10.18260/1-2--33231

Subject(s) - electronic circuit , argument (complex analysis) , computer science , calculus (dental) , regression analysis , metric (unit) , surprise , logistic regression , mathematics education , mathematics , psychology , engineering , electrical engineering , machine learning , chemistry , medicine , social psychology , biochemistry , operations management , dentistry

The ability to predict future engineering student performance based upon previous academic performance would be a useful tool for identifying at-risk students and increasing retention in engineering programs. Student persistence in engineering programs relates to previous course performance [1]. Many courses offered in engineering programs occur in specific sequences such that one course can have several prerequisites. An analysis of prerequisite course performance can be useful for predicting future student performance [2]. In fact, studies have shown that pre-college academic performance can be a predictor of program graduation [3].

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