Date of Award

Fall 2014

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

STEM and Professional Studies

Program/Concentration

Occupational and Technical Studies

Committee Director

John M. Ritz

Committee Member

Steve Hsiung

Committee Member

Darryl Draper

Abstract

The intent of this dissertation was to identify the cognitive processes used by advanced pre-engineering students to solve complex engineering design problems. Students in technology and engineering education classrooms are often taught to use an ideal engineering design process that has been generated mostly by educators and curriculum developers. However, the review of literature showed that it is unclear as to how advanced pre-engineering students cognitively navigate solving a complex and multifaceted problem from beginning to end. Additionally, it was unclear how a student thinks and acts throughout their design process and how this affects the viability of their solution. Therefore, Research Objective 1 was to identify the fundamental cognitive processes students use to design, construct, and evaluate operational solutions to engineering design problems. Research Objective 2 was to determine identifiers within student cognitive processes for monitoring aptitude to successfully design, construct, and evaluate technological solutions. Lastly, Research Objective 3 was to create a conceptual technological and engineering problem-solving model integrating student cognitive processes for the improved development of problem-solving abilities.

The methodology of this study included multiple forms of data collection. The participants were first given a survey to determine their prior experience with engineering and to provide a description of the subjects being studied. The participants were then presented an engineering design challenge to solve individually. While they completed the challenge, the participants verbalized their thoughts using an established "think aloud" method. These verbalizations were captured along with participant observational recordings using point-of-view camera technology. Additionally, the participant design journals, design artifacts, solution effectiveness data, and teacher evaluations were collected for analysis to help achieve the research objectives of this study. Two independent coders then coded the video/audio recordings and the additional design data using Halfin's (1973) 17 mental processes for technological problem-solving.

The results of this study indicated that the participants employed a wide array of mental processes when solving engineering design challenges. However, the findings provide a general analysis of the number of times participants employed each mental process, as well as the amount of time consumed employing the various mental processes through the different stages of the engineering design process. The results indicated many similarities between the students solving the problem, which may highlight voids in current technology and engineering education curricula. Additionally, the findings showed differences between the processes employed by participants that created the most successful solutions and the participants who developed the least effective solutions. Upon comparing and contrasting these processes, recommendations for instructional strategies to enhance a student's capability for solving engineering design problems were developed. The results also indicated that students, when left without teacher intervention, use a simplified and more natural process to solve design challenges than the 12-step engineering design process reported in much of the literature. Lastly, these data indicated that students followed two different approaches to solving the design problem. Some students employed a sequential and logical approach, while others employed a nebulous, solution centered trial-and-error approach to solving the problem. In this study the participants who were more sequential had better performing solutions. Examining these two approaches and the student cognition data enabled the researcher to generate a conceptual engineering design model for the improved teaching and development of engineering design problem solving.

DOI

10.25777/zzbj-b616

ISBN

9781321558517

Share

COinS