Date of Award:

12-2011

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Applied Sciences, Technology, and Education

Department name when degree awarded

Engineering and Technology Education

Committee Chair(s)

Ning Fang

Committee

Ning Fang

Committee

Kurt Becker

Committee

Oenardi Lawanto

Committee

Edward Reeve

Committee

Wenbin Yu

Abstract

Engineering dynamics is a fundamental sophomore-level course required for many engineering students. This course is also one of the most challenging courses in which many students fail because it requires students to have not only solid mathematical skills but also a good understanding of dynamics concepts and principles.

The overall goal of this study was to develop a validated set of mathematical models to predict student academic performance in an engineering dynamics course taught in the College of Engineering at Utah State University. The predictive models will help the instructor to understand how well or how poorly the students in his/her class will perform, and hence the instructor can choose proper pedagogical and instructional interventions to enhance student learning outcomes.

In this study, 24 predictive models are developed by using four mathematical modeling techniques and a variety of combinations of eight predictor variables. The eight predictor variables include students’ cumulative GPA, grades in four prerequisite courses, and scores in three dynamics mid-term exams. The results and analysis show that each of the four mathematical modeling techniques have an average prediction accuracy of more than 80%, and that the models with the first six predictor variables yield high prediction accuracy and leave sufficient time for the instructor to implement educational interventions.

Checksum

adc699a6db89d8de999d4626558ea6e5

Comments

Publication made available electronically December 21, 2011.

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