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There is a lack of knowledge on the way transportation engineering practitioners engage with various Contextual Representations (CRs) to solve traffic engineering design problems. CRs such as equations, graphs, and tables could be perceived differently, even if they represent the same concept. The present study recognized left-turn treatment at signalized intersections as a prominent concept in traffic engineering practice and identified three associated CRs (a text-book equation, a graphical representation, and a stepwise flowchart) to design a phasing plan. Two data collection mechanisms were concurrently employed: 1) eye-tracking to analyze visual attention and document problem-solving approaches and 2) reflective clinical interviews to analyze ways of thinking and document problem-solving rationales. The problem-solving experiment was completed by twenty-four transportation engineering practitioners. Transportation engineering practitioners not only demonstrated preferences for different CRs, they also demonstrated different reasoning as to the selection of the same CR. Results of Multivariate Analysis of Variance showed that there was a statistically significant difference in visual attention based on CR. Additionally, in-vivo coding of participants’ interviews identified seven distinct rationales for CR selection. Findings from this study could be employed to modify transportation engineering curricula with optimized visual CRs.

 

Problem solvers vary their approaches to solving problems depending on the context of the problem, the requirements of the solution, and the ways in which the problems and material to solve the problem are represented, or representations. Representations take many forms (i.e. tables, graphs, figures, images, formulas, visualizations, and other similar contexts) and are used to communicate information to a problem solver. Engagement with certain representations varies between problem solvers and can influence design and solution quality. A problem solver’s evaluation of representations and the reasons for using a representation can be considered factors in problem-solving heuristics. These factors describe unique problem-solving behaviors that can help understand problem solvers. These behaviors may lead to important relationships between a problem solver’s decisions and their ability to solve a problem and overall quality of the solution. Therefore, we pose the following research question: How do factors of problem-solving heuristics describe the unique behaviors of engineering students as they solve multiple problems? To answer this question, we interviewed 16 undergraduate engineering students studying civil engineering. The interviews consisted of a problem-solving portion that was followed immediately by a semi-structured retrospective interview with probing questions created based on the real time monitoring of the problem-solving interview using eye tracking techniques. The problem-solving portion consisted of solving three problems related to the concept of headloss in fluid flow through pipes. Each of the three problems included the same four representations that were used by the students as approaches to solving the problem. The representations are common ways to present the concept of headloss in pipe flow and included two formulas, a set of tables, and a graph. This paper presents a set of common reasons for why decisions were made during the problem-solving process that help to understand more about the problem-solving behavior of engineering students. 

Engineering practitioners solve problems in various ways; it is plausible that they often rely on graphs, figures, formulas and other representations to reach a solution. How and why engineering practitioners use representations to solve problems can characterize certain problem-solving behaviors, which can be used to determine particular types of problem solvers. The purpose of this research was to determine the relationship between time spent referring to various representations and the justifications for the decisions made during the problem-solving process of engineering practitioners. A persona-based approach was used to characterize the problem-solving behavior of 16 engineering practitioners. Utilizing eye tracking and retrospective interview techniques, the problem-solving process of engineering practitioners was explored. Three unique problem-solver personas were developed that describe the behaviors of engineering practitioners; a committed problem solver, an evaluative problem, and an indecisive problem solver. The three personas suggest that there are different types of engineering practitioner problem solvers. This study contributes to engineering education research by expanding on problem-solving research to look for reasons why decisions are made during the problem-solving process. Understanding more about how the differences between problem solvers affect the way they approach a problem and engage with the material presents a more holistic view of the problem-solving process of engineering practitioners. 

This paper presents the preliminary findings of a larger study on the problem-solving rationale associated with the use of multiple contextual representations. Four engineering practitioners solved a problem associated with headloss in pipe flow while their visual attention was tracked using eye tracking technology. Semi-structured interviews were conducted following the problem-solving interview and the rationale associated with their decisions to use a particular contextual representation emerged. The results of this study show how the rationale can influence the problem-solving process of the four engineering practitioners. Engineering practitioners used various contextual representations and provided multiple rationale for their decisions. Eye tracking techniques and semi-structured interviews created a robust picture of the problem-solving process that supplements previous problem-solving research.