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1. How Do Engineering Undergraduates Define Engineering Identity?

   Tallman, Brett; Schell, William J.; Sybesma, Tessa A.; Kwapisz, Monika B.; Hughes, Bryce E.; Bozic, Christy; Kotys-Schwartz, Daria (October 2019, American Society for Engineering Management)

   Identity is central to learning in engineering, and development of an engineering identity is a core aspect of the education and training process for engineers. Identification with engineering has been shown to assist in the recruitment of diverse students into the field and improve student retention among all groups. For these reasons, engineering educators should consider the formation of professional identity as they make decisions about how to best prepare graduates to enter their chosen fields. In order to utilize professional identity, these educators must understand how their students conceptualize engineering identity. This work seeks to promote that understanding by investigating conceptualizations undergraduate engineers hold of engineering identity, their self-view as engineers, and the sources of these views. Using data collected from pilot focus groups of ten undergraduate students at two different U.S. engineering schools, we applied a grounded theory approach to explore these topics. This exploration found many elements consistent with prior work in engineering identity (e.g. a focus on technical knowledge defining engineering) while adding new elements, such as students’ surprise at the breadth of engineering and desire to use their engineering education as a vehicle to positively impact the world. 

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2. Identifying common perceived stressors and stress-relief strategies among undergraduate engineering students

   Ban, N.; Shannon, H.; Wright, C.J.; Miller, M.E.; Hargis, L.E.; Usher, E.L.; Hammer, J.H.; Wilson, S.A. (January 2022, ASEE annual conference exposition)

   Mental health concerns have become a growing problem among collegiate engineering students. To date, there has been little research to understand the factors that influence student mental health within this population. Literature on engineering student mental health supports the idea that engineering students experience high levels of mental health distress, which often stems from stressors such as academic workload, maintaining a strong grade point average (GPA), and pressure from parents and/or professors. Of particular concern, distressed engineering students are less likely to seek professional help when compared to students in other majors. As a result, a comprehensive study was conducted on engineering mental health help-seeking behavior. Through secondary analysis of the data from that study, this work aims to identify common perceived stressors that may contribute to mental health distress, as well as perceived coping strategies that may be used instead of seeking professional mental health help. A diverse group of 33 engineering undergraduate students were a part of the comprehensive study on engineering mental health help-seeking behavior. For this study, qualitative data was analyzed to address two specific research questions: 1) What are the main sources of stress that engineers have experienced in their engineering training? and 2) What coping strategies have students developed as an alternative to seeking professional help? Several common perceived stressors were identified including an unsupportive and challenging engineering training environment, challenges in time management, and academic performance expectations. Perceived coping strategies identified include relationships with family, friends, and classmates and health and wellness activities such as exercise, mindfulness, and maintaining spiritual health. The results of this work will be helpful in recognizing ways to improve engineering education and increase student support. 

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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. Types of Models Identified by First-Year Engineering Students

   Rodgers, Kelsey J.; Thompson, Angela; Verleger, Matthew A.; Marbouti, Farshid; Shah, Nishith; Thaker, Pujan (July 2021, Zone 1 Conference of the American Society for Engineering Education)

   null (Ed.)

   Understanding models is important for engineering students, but not often taught explicitly in first-year courses. Although there are many types of models in engineering, studies have shown that engineering students most commonly identify prototyping or physical models when asked about modeling. In order to evaluate students’ understanding of different types of models used in engineering and the effectiveness of interventions designed to teach modeling, a survey was developed. This paper describes development of a framework to categorize the types of engineering models that first-year engineering students discuss based on both previous literature and students’ responses to survey questions about models. In Fall 2019, the survey was administered to first-year engineering students to investigate their awareness of types of models and understanding of how to apply different types of models in solving engineering problems. Students’ responses to three questions from the survey were analyzed in this study: 1. What is a model in science, technology, engineering, and mathematics (STEM) fields?, 2. List different types of models that you can think of., and 3. Describe each different type of model you listed. Responses were categorized by model type and the framework was updated through an iterative coding process. After four rounds of analysis of 30 different students’ responses, an acceptable percentage agreement was reached between independent researchers coding the data. Resulting frequencies of the various model types identified by students are presented along with representative student responses to provide insight into students’ understanding of models in STEM. This study is part of a larger project to understand the impact of modeling interventions on students’ awareness of models and their ability to build and apply models. 

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