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1. Comparing engineering students’ and professionals’ conceptions of ambiguity.

   https://doi.org/10.1109/FIE56618.2022.9962415  

   Douglas, E. P. (January 2022, Proceedings of the Frontiers in Education Conference. (41696 2022. IEEE).)

   Abstract— Engineers are frequently confronted with complex, unique, and challenging problems. Many of our most pressing engineering problems contain ambiguous elements, and a core activity of engineering is being able to solve these complex problems effectively. While engineering problems are often described as ambiguous, ambiguity has not been clearly defined in the literature in the context of engineering problem solving. This work-in-progress paper describes our initial results to understand how ambiguity is experienced during engineering problem solving. We interviewed both engineering students and engineering professionals about ambiguous problems they have encountered. We found that both groups identified technical ambiguity as the core element of engineering problem solving. They also described differences between classroom and workplace problems, with students describing classroom problems as “purposefully” ambiguous. Students had strong negative emotional reactions to ambiguity, in contrast to professionals who seemed to accept ambiguity as a common element in engineering problem. Our initial findings suggest that changes to engineering education practice that allow students to become comfortable with ambiguity would better prepare them for the ambiguous problems they will face in the workplace. Keywords—problem solving, ambiguity, qualitative 

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2. Factors Contributing to the Problem-Solving Heuristics of Civil Engineering Students

   Geston, Sean L.; Brown, Shane; Barner, Matthew S.; Abadi, Masoud G.; Hurwitz, David S. (January 2019, ASEE annual conference & exposition)

   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. 

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3. Developing Deliberate Practice for Learning Engineering Dynamics by Analyzing Students’ Mental Models

   Tang, Yan; Bai, Haiyan; Catrambone, Richard (June 2022, ASEE annual conference exposition)

   Practice plays a critical role in learning engineering dynamics. Typical practice in a dynamics course involves solving textbook problems. These problems can impose great cognitive load on underprepared students because they have not mastered constituent knowledge and skills required for solving whole problems. For these students, learning can be improved by being engaged in deliberate practice. Deliberate practice refers to a type of practice aimed at improving specific constituent knowledge or skills. Compared to solving whole problems requiring the simultaneous use of multiple constituent skills, deliberate practice is usually focused on one component skill at a time, which results in less cognitive load and more specificity. Contemporary theories of expertise development have highlighted the influence of deliberate practice (DP) on achieving exceptional performance in sports, music, and various professional fields. Concurrently, there is an emerging method for improving learning efficiency of novices by combining deliberate practice with cognitive load theory (CLT), a cognitive-architecture-based theory for instructional design. Mechanics is a foundation for most branches of engineering. It serves to develop problem-solving skills and consolidate understanding of other subjects, such as applied mathematics and physics. Mechanics has been a challenging subject. Students need to understand governing principles to gain conceptual knowledge and acquire procedural knowledge to apply these principles to solve problems. Due to the difficulty in developing conceptual and procedural knowledge, mechanics courses are among those that receive high DFW rates (percentage of students receiving a grade of D or F or Withdrawing from a course), and students are more likely to leave engineering after taking mechanics courses. Deliberate practice can help novices develop good representations of the knowledge needed to produce superior problem solving performance. The goal of the present study is to develop deliberate practice techniques to improve learning effectiveness and to reduce cognitive load. Our pilot study results revealed that the student mental effort scores were negatively correlated with their knowledge test scores with r = -.29 (p < .05) after using deliberate practice strategies. This supports the claim that deliberate practice can improve student learning while reducing cognitive load. In addition, the higher the students’ knowledge test scores, the lower their mental effort was when taking the tests. In other words, the students who used deliberate practice strategies had better learning results with less cognitive load. To design deliberate practice, we often need to analyze students’ persistent problems caused by faulty mental models, also referred to as an intuitive mental model, and misconceptions. In this study, we continue to conduct an in-depth diagnostic process to identify students’ common mistakes and associated intuitive mental models. We then use the results to develop deliberate practice problems aimed at changing students’ cognitive strategies and mental models. 

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4. Developing Deliberate Practice for Learning Engineering Dynamics by Analyzing Students’ Mental Models

   Tang, Yan; Bai, Haiyan; Catrambone, Richard (June 2022, ASEE annual conference exposition)

   Practice plays a critical role in learning engineering dynamics. Typical practice in a dynamics course involves solving textbook problems. These problems can impose great cognitive load on underprepared students because they have not mastered constituent knowledge and skills required for solving whole problems. For these students, learning can be improved by being engaged in deliberate practice. Deliberate practice refers to a type of practice aimed at improving specific constituent knowledge or skills. Compared to solving whole problems requiring the simultaneous use of multiple constituent skills, deliberate practice is usually focused on one component skill at a time, which results in less cognitive load and more specificity. Contemporary theories of expertise development have highlighted the influence of deliberate practice (DP) on achieving exceptional performance in sports, music, and various professional fields. Concurrently, there is an emerging method for improving learning efficiency of novices by combining deliberate practice with cognitive load theory (CLT), a cognitive-architecture-based theory for instructional design. Mechanics is a foundation for most branches of engineering. It serves to develop problem-solving skills and consolidate understanding of other subjects, such as applied mathematics and physics. Mechanics has been a challenging subject. Students need to understand governing principles to gain conceptual knowledge and acquire procedural knowledge to apply these principles to solve problems. Due to the difficulty in developing conceptual and procedural knowledge, mechanics courses are among those that receive high DFW rates (percentage of students receiving a grade of D or F or Withdrawing from a course), and students are more likely to leave engineering after taking mechanics courses. Deliberate practice can help novices develop good representations of the knowledge needed to produce superior problem solving performance. The goal of the present study is to develop deliberate practice techniques to improve learning effectiveness and to reduce cognitive load. Our pilot study results revealed that the student mental effort scores were negatively correlated with their knowledge test scores with r = -.29 (p < .05) after using deliberate practice strategies. This supports the claim that deliberate practice can improve student learning while reducing cognitive load. In addition, the higher the students’ knowledge test scores, the lower their mental effort was when taking the tests. In other words, the students who used deliberate practice strategies had better learning results with less cognitive load. To design deliberate practice, we often need to analyze students’ persistent problems caused by faulty mental models, also referred to as an intuitive mental model, and misconceptions. In this study, we continue to conduct an in-depth diagnostic process to identify students’ common mistakes and associated intuitive mental models. We then use the results to develop deliberate practice problems aimed at changing students’ cognitive strategies and mental models. 

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5. BOARD # 438: Research Initiation: Facilitating Knowledge Transfer within Engineering Curricula

   https://doi.org/10.18260/1-2--55817  

   De_Rosa, Alexander; Reed, Teri; Van_Horne, Samuel (June 2025, ASEE Conferences)

   It is well-established that students have difficulty transferring theory and skills between courses in their undergraduate curriculum. At the same time, many college-level courses only concern material relating to the course itself and do not cover how this material might be used elsewhere. It is unsurprising, then, that students are unable to transfer and integrate knowledge from multiple areas into new problems as part of capstone design courses, for example, or in their careers. More work is required to better enable students to transfer knowledge between their courses, learn skills and theory more deeply, and to form engineers who are better able to adapt to new situations and solve “systems-level” problems. Various authors in both the cognitive and disciplinary sciences have discussed these difficulties with the transfer of knowledge, and noted the need to develop tools and techniques for promoting knowledge transfer, as well as to help students develop cross-course connections. This work aimed to address these barriers to knowledge transfer, and crucially develop the needed activities and practices for promoting transfer by answering the following research questions: (1) What are the primary challenges experienced by students when tasked with transferring theory and skills from prior courses, specifically mathematics and physics? (2) What methods of prior knowledge activation are most effective in enabling students to apply this prior knowledge in new areas of study? In this paper we present a holistic summary of the work completed under this award. Initially, findings from a series of n=23 think aloud interviews, in which participants were asked to solve a typical engineering statics problem, is presented. These interviews evidenced multiple barriers to knowledge transfer (lack of prior knowledge, accuracy of prior knowledge, conceptual understanding, lack of teaching of applications, language of problem, curricular mapping) that hindered participant success in terms of using their mathematical skills to solve the problem. Findings also indicated the importance of reflective thinking on behalf of the participants to their problem solving success. Based on this initial work using think alouds, a further set of interviews (n=8) were conducted to more deeply examine student conceptions of important mathematical topics that are transferred into engineering such as integration and centroids. Findings indicated that participant knowledge and understanding of centroids in particular was generally based around more intuitive or geometrical conceptions rather than concrete physical or mathematical models. Following up on the initial study of problem solving, the importance of reflection on behalf of the problem solver was also examined in more detail. Comparison of expert (faculty) and novice (student) approaches to problem solving demonstrates how often experts reflect on their progress during the solving process and the manner in which they are able to connect problems in one context to similar problems they have encountered in the past in other areas of engineering. The ability of experts to “chunk” problems into smaller stages and reflect on individual elements of the problem at hand rather than the problem as a whole was also seen to be a differentiating factor in their approach as compared to novices. Similar to this paper, the associated poster presentation will cover a holistic representation of the findings of this study. 

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