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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 knowledgemore »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.« less
2. Task-Analysis-Guided Deliberate Practice for Learning Free-Body Diagrams

   Tang, Yan ; Hai, Haiyan ; Catrambone, Richard ( July 2021 , ASEE annual conference proceedings)

   Free-body diagrams (FBDs) are diagrammatic representations of external forces and moments exerted on an object of interest for solving kinetics problems. Several studies have reported different ways of teaching FBDs in terms of pictorial representation of forces (e.g., placement of vectors or labeling). However, there is little research on practice strategies for helping students learn how to draw FBDs. Through the use of task analysis and a model of subgoal learning, we will develop task-analysis-guided deliberate practice to enhance learning. Task analysis is often used in instructional design to extract knowledge requirements for acquiring a skill. Skill acquisition is usually divided into three phases including declarative, knowledge compilation, and procedural. Task analysis in our study will identify relevant declarative and procedural knowledge related to drawing FBDs. The findings will be used to develop deliberate practice. Deliberate practice can help novices develop good representations of the knowledge needed to produce superior problem solving performance. This has been viewed as a gold standard for practice. Although deliberate practice is mainly studied among elite performers, the recent literature has revealed promising results for novices. Considering cognitive capacity limitations, we will apply cognitive load theory to develop deliberate practice to help students build declarativemore »and procedural knowledge without exceeding their working memory limitations. A knowledge extraction expert will take an iterative approach to conduct task analyses with a subject matter expert (or experts)to distill knowledge to a level that is appropriate for students in the dynamics course. We will then integrate the task analysis results with instructional design strategies derived from cognitive load theory and the subgoal learning model to develop deliberate practice and assessment materials. Examples and assessment results will be provided to evaluate the effectiveness of the instructional design strategies as well as the challenges.« less
3. Best 2019 PIC III Paper: Do They Understand Your Language? Access Their Fluency with Vector Representation

   https://doi.org/10.18260/1-2--34205  

   Davishahl, Eric ; Haskell, Todd ; Davishahl, Jill ; Singleton, Lee ; Goodridge, Wade ( June 2020 , 2020 ASEE Virtual Annual Conference Content Access)

   In teaching mechanics, we use multiple representations of vectors to develop concepts and analysis techniques. These representations include pictorials, diagrams, symbols, numbers and narrative language. Through years of study as students, researchers, and teachers, we develop a fluency rooted in a deep conceptual understanding of what each representation communicates. Many novice learners, however, struggle to gain such understanding and rely on superficial mimicry of the problem solving procedures we demonstrate in examples. The term representational competence refers to the ability to interpret, switch between, and use multiple representations of a concept as appropriate for learning, communication and analysis. In engineering statics, an understanding of what each vector representation communicates and how to use different representations in problem solving is important to the development of both conceptual and procedural knowledge. Science education literature identifies representational competence as a marker of true conceptual understanding. This paper presents development work for a new assessment instrument designed to measure representational competence with vectors in an engineering mechanics context. We developed the assessment over two successive terms in statics courses at a community college, a medium-sized regional university, and a large state university. We started with twelve multiple-choice questions that survey the vector representations commonlymore »employed in statics. Each question requires the student to interpret and/or use two or more different representations of vectors and requires no calculation beyond single digit integer arithmetic. Distractor answer choices include common student mistakes and misconceptions drawn from the literature and from our teaching experience. We piloted these twelve questions as a timed section of the first exam in fall 2018 statics courses at both Whatcom Community College (WCC) and Western Washington University. Analysis of students’ unprompted use of vector representations on the open-ended problem-solving section of the same exam provides evidence of the assessment’s validity as a measurement instrument for representational competence. We found a positive correlation between students’ accurate and effective use of representations and their score on the multiple choice test. We gathered additional validity evidence by reviewing student responses on an exam wrapper reflection. We used item difficulty and item discrimination scores (point-biserial correlation) to eliminate two questions and revised the remaining questions to improve clarity and discriminatory power. We administered the revised version in two contexts: (1) again as part of the first exam in the winter 2019 Statics course at WCC, and (2) as an extra credit opportunity for statics students at Utah State University. This paper includes sample questions from the assessment to illustrate the approach. The full assessment is available to interested instructors and researchers through an online tool.« less
4. Do They Understand Your Language? Assess Their Fluency with Vector Representations

   Davishahl, E. ; Haskell, T. R. ; Davishahl, J. ; Singleton, L. ; Goodridge, W. H. ( June 2019 , ASEE Annual Conference proceedings)

   In teaching mechanics, we use multiple representations of vectors to develop concepts and analysis techniques. These representations include pictorials, diagrams, symbols, numbers and narrative language. Through years of study as students, researchers, and teachers, we develop a fluency rooted in a deep conceptual understanding of what each representation communicates. Many novice learners, however, struggle to gain such understanding and rely on superficial mimicry of the problem solving procedures we demonstrate in examples. The term representational competence refers to the ability to interpret, switch between, and use multiple representations of a concept as appropriate for learning, communication and analysis. In engineering statics, an understanding of what each vector representation communicates and how to use different representations in problem solving is important to the development of both conceptual and procedural knowledge. Science education literature identifies representational competence as a marker of true conceptual understanding. This paper presents development work for a new assessment instrument designed to measure representational competence with vectors in an engineering mechanics context. We developed the assessment over two successive terms in statics courses at a community college, a medium-sized regional university, and a large state university. We started with twelve multiple-choice questions that survey the vector representations commonlymore »employed in statics. Each question requires the student to interpret and/or use two or more different representations of vectors and requires no calculation beyond single digit integer arithmetic. Distractor answer choices include common student mistakes and misconceptions drawn from the literature and from our teaching experience. We piloted these twelve questions as a timed section of the first exam in fall 2018 statics courses at both Whatcom Community College (WCC) and Western Washington University. Analysis of students’ unprompted use of vector representations on the open-ended problem-solving section of the same exam provides evidence of the assessment’s validity as a measurement instrument for representational competence. We found a positive correlation between students’ accurate and effective use of representations and their score on the multiple choice test. We gathered additional validity evidence by reviewing student responses on an exam wrapper reflection. We used item difficulty and item discrimination scores (point-biserial correlation) to eliminate two questions and revised the remaining questions to improve clarity and discriminatory power. We administered the revised version in two contexts: (1) again as part of the first exam in the winter 2019 Statics course at WCC, and (2) as an extra credit opportunity for statics students at Utah State University. This paper includes sample questions from the assessment to illustrate the approach. The full assessment is available to interested instructors and researchers through an online tool.« less
5. Conceptual Representations in the Workplace and Classroom Settings: A Comparative Ethnography

   Barner, M. S. Brown ( June 2019 , ASEE Annual Conference proceedings)

   The following is a Theory paper that presents an ethnographic exploration into how concepts are situated in workplace and classroom settings. Situated cognition research demonstrates that different contexts wherein learning occurs and knowledge is applied shape our conceptual understanding. Within engineering education and practice this means that practitioners, students, and instructors demonstrate different ways of representing their conceptual knowledge due to the different contexts wherein they learn and apply engineering concepts. The purpose of this paper is to present themes on how practitioners, students, and instructors represent fundamental structural engineering concepts within the contexts of structural engineering design. By representation of concepts we mean the ways in which practitioners, students, and instructors portray and demonstrate their conceptual understanding of concepts through the social and material contexts of the workplace and classroom environments. Previous research on learning and engineering education has shown the influence that social and material contexts within these environments have on our knowing and understanding. The researchers use ethnographic methods consisting of workplace and classroom observations, interviews with practitioners, students, and instructors, and documentation of workplace and academic artifacts—such as drawings, calculations, and notes—to access practitioners’, students’, and instructors’ conceptual representations. These ethnographic methods are conducted at amore »private engineering firm and in 300 and 400 level structural engineering courses. Preliminary results indicate that instructors’ conceptual representations in the classroom aim to enhance students’ broader understanding of these concepts; whereas students’ conceptual representations are focused towards utility in solving homework and exam problems. Practitioners’ conceptual representations are more flexible and adapt to project and workplace constraints. These results seem to indicate that even when instructors emphasize broader conceptual knowledge, the academic incentives behind homework and test scores lead to more academically focused conceptual representations by students. Furthermore, practitioners’ conceptual representations indicate the necessity of conceptual fluency in the workplace, which contrasts with the rigidity of conceptual representations that students develop in the classroom. This comparison between workplace and academic conceptual representations enhances our understanding of the extent to which students, instructors, and practitioners share similar or different conceptual representations within the domain of structural engineering. This, in turn, may lead to guided curriculum reform efforts aimed at better preparing structural engineering students for their professional careers.« less