Date of Award

2013

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer and Information Technology

First Advisor

Alejandra Magana

Committee Chair

Alejandra Magana

Committee Member 1

Edwin Garcia

Committee Member 2

Grant Richards

Committee Member 3

James Mohler

Abstract

Technology is becoming a more critical agent for supporting learning as well as research in science and engineering. In particular, technology-based tools in the form of simulations and virtual environments support learning using mathematical models and computational methods. The purpose of this research is to: (a) measure the value added in conveying Thermodynamics of materials concepts with a blended learning environment using computational simulation tools with lectures; and (b) characterize students' use of representational forms to convey their conceptual understanding of core concepts within a learning environment that blended Gibbs computational resource and traditional lectures.

A mix-method approach was implemented that included the use of statistical analysis to compare student test performance as a result of interacting with Gibbs tool and the use of Grounded Theory inductive analysis to explore students' use of representational forms to express their understanding of thermodynamics of material concepts. Results for the quantitative study revealed positive gains in students' conceptual understanding before and after interacting with Gibbs tool for the majority of the concepts tested. In addition, insight gained from the qualitative analysis helped provide understanding about how students utilized representational forms in communicating their understanding of thermodynamics of material concepts. Knowledge of how novice students construct meaning in this context will provide insight for engineering education instructors and researchers in understanding students' learning processes in the context of educational environments that integrate expert simulation tools as part of their instructional resources for foundational domain knowledge.

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