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1. 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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2. 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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3. Bridging the Gap Between Academia and Industry in Approaches for Solving Ill-Structured Problems: Problem Formulation and Protocol Development

   Akinci-Ceylan, Secil ; Cetin, Kristen Sara ; Fleming, Renee ; Ahn, Benjamin ; Surovek, Andrea ; Cetin, Bora ; Taylor, Paige ( January 2018 , ASEE Annual Conference proceedings)

   One of the main skills of engineers is to be able to solve problems. It is generally recognized that real-world engineering problems are inherently ill-structured in that they are complex, defined by non-engineering constraints, are missing information, and contain conflicting information. Therefore, it is very important to prepare future engineering students to be able to anticipate the occurrence of such problems, and to be prepared to solve them. However, most courses are taught by academic professors and lecturers whose focus is on didactic teaching of fundamental principles and code-based design approaches leading to predetermined “right” answers. Most classroom-taught methods to solve well-structured problems and the methods needed to solve ill-structured problems are strikingly different. The focus of our current effort is to compare and contrast the problem solving approaches employed by students, academics and practicing professionals in an attempt to determine if students are developing the necessary skills to tackle ill-structured problems. To accomplish this, an ill-structured problem is developed, which will later be used to determine, based on analysis of oral and written responses of participants in semi-structured interviews, attributes of the gap between student, faculty, and professional approaches to ill-structured problem solving. Based on the results of this analysis, we will identify what pedagogical approaches may limit and help students’ abilities to develop fully-formed solutions to ill-structured problems. This project is currently ongoing. This work-in-progress paper will present the study and proposed methods. Based on feedback obtained at the conference from the broader research community, the studies will be refined. The current phase includes three parts, (1) problem formulation; (2) protocol development; and (3) pilot study. For (1), two different ill-structured problems were developed in the Civil Engineering domain. The problem difficulty assessment method was used to determine the appropriateness of each problem developed for this study. For (2), a protocol was developed in which participants will be asked to first solve a simple problem to become familiar with the interview format, then are given 30 minutes to solve the provided ill-structured problem, following a semi-structured interview format. Participants will be encouraged to speak out loud and also write down what they are thinking and their thought processes throughout the interview period. Both (1) and (2) will next be used for (3) the pilot study. The pilot study will include interviewing three students, three faculty members and three professional engineers. Each participant will complete both problems following the same protocol developed. Post-interview discussion will be held with the pilot study participants individually to inquire if there were any portions of the tasks that are unclearly worded or could be improved to clarify what was being asked. Based on these results the final problem will be chosen and refined. 

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4. Investigating the Relationship Between Spatial Skills and Engineering Design

   Raju, G. ( January 2022 , ICERI Intenational Conference on Educational Research and Innovation Conference Proceedings)

   Design is a core attribute of engineering practice; in fact, the etymology of the word engineer is traced to the Latin “ingeniare,” which translates as inventor or designer. In order to prepare our students for success in an engineering career, they must be proficient at design and able to think creatively and flexibly about optimal solutions to problems. Additionally, numerous studies have demonstrated the need for well-developed spatial skills for success in engineering, especially in engineering problem solving. Studies also show a link between spatial thinking and technical creativity. The focus of this research is on understanding the relationship between spatial visualization skills and engineering design. As a first phase of testing, 127 undergraduate engineering students completed four tests of spatial ability. In a second phase, 102 students returned and were asked to complete three tasks. The first was designing a ping pong ball launcher to hit a target at a specific height from a given distance. They were then asked to list as many factors as possible that should be considered when designing a retaining wall for mitigating flood damage along the Mississippi River in the Midwest in the United States. The third task was to sketch as many ideas as possible in a given timeframe for a rainwater collection system in a remote location. This research paper will present preliminary analysis of the Midwest Flood problem and spatial ability data. The initial insights will be discussed relative to the overall study and how this work could inform undergraduate engineering education, and specifically the provision of design education. 

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5. Changing Homework Achievement with Mechanix Pedagogy: Increasing the Efficacy of a Measurement Tool for Construction Majors

   Talley, Kimberly G. ; Hurt, Josh ; Linsey, Julie S. ( June 2022 , ASEE Annual Conference proceedings)

   In online or large in-person course sections, instructors often adopt an online homework tool to alleviate the burden of grading. While these systems can quickly tell students whether they got a problem correct for a multiple-choice or numeric answer, they are unable to provide feedback on students’ free body diagrams. As the process of sketching a free body diagram correctly is a foundational skill to solving engineering problems, the loss of feedback to the students in this area is a detriment to students. To address the need for rapid feedback on students’ free body diagram sketching, the research team developed an online, sketch-recognition system called Mechanix. This system allows students to sketch free body diagrams, including for trusses, and receive instant feedback on their sketches. The sketching feedback is ungraded. After the students have a correct sketch, they are then able to enter in the numeric answers for the problem and submit those for a grade. Thereby, the platform offers the grading convenience of other online homework systems but also helps the students develop their free body diagram sketching skills. To assess the efficacy of this experimental system, standard concept inventories were administered pre- and post-semester for both experimental and control groups. The unfamiliarity or difficulty of some advanced problems in the Statics Concept Inventory, however, appeared to discourage students, and many would stop putting in any effort after a few problems that were especially challenging to solve. This effect was especially pronounced with the Construction majors versus the Mechanical Engineering majors in the test group. To address this tendency and therefore collect more complete pre- and post-semester concept inventory data, the research group worked on reordering the Statics Concept Inventory questions from more familiar to more challenging, based upon the past performance of the initial students taking the survey. This paper describes the process and results of the effort to reorder this instrument in order to increase Construction student participation and, therefore, the researchers’ ability to measure the impact of the Mechanix system. 

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