By Topic

Work in progress - a decision tree approach to predicting student performance in a high-enrollment, high-impact, and core engineering course

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Ning Fang ; Dept. of Eng. & Technol. Educ., Utah State Univ., Logan, UT, USA ; Jingui Lu

This paper aims at developing a decision tree model to predict student performance in engineering dynamics - a high-enrollment, high-impact, and core engineering course. This study is innovative because no prior literature exists on the same topic. Three research contributions are made: 1) Nine ¿if-then¿ decision rules were generated to predict student performance in engineering dynamics. 2) It is revealed that a student's score in statics and cumulative GPA play a significant role in governing student performance in engineering dynamics. 3) It is revealed that the decision tree predictions are more accurate than the predictions from the traditional multivariate linear regression technique.

Published in:

Frontiers in Education Conference, 2009. FIE '09. 39th IEEE

Date of Conference:

18-21 Oct. 2009