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1. Problem Solving in Genetics: Content Hints Can Help

   https://doi.org/10.1187/cbe.18-06-0093  

   Avena, Jennifer S.; Knight, Jennifer K.; Batzli, Janet (June 2019, CBE—Life Sciences Education)

   Problem solving is an integral part of doing science, yet it is challenging for students in many disciplines to learn. We explored student success in solving genetics problems in several genetics content areas using sets of three consecutive questions for each content area. To promote improvement, we provided students the choice to take a content-focused prompt, termed a “content hint,” during either the second or third question within each content area. Overall, for students who answered the first question in a content area incorrectly, the content hints helped them solve additional content-matched problems. We also examined students’ descriptions of their problem solving and found that students who improved following a hint typically used the hint content to accurately solve a problem. Students who did not improve upon receipt of the content hint demonstrated a variety of content-specific errors and omissions. Overall, ultimate success in the practice assignment (on the final question of each topic) predicted success on content-matched final exam questions, regardless of initial practice performance or initial genetics knowledge. Our findings suggest that some struggling students may have deficits in specific genetics content knowledge, which when addressed, allow the students to successfully solve challenging genetics problems. 

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2. A Phenomenological Study of Expert Problem Solving

   https://doi.org/10.18260/1-2--45703  

   De_Rosa, Alexander; Reed, Teri (April 2024, ASEE Conferences)

   I initially became interested in knowledge transfer after observing my students’ general inability to use mathematical knowledge and skills in an applied (engineering) context. My personal belief was that the students should have an understanding of basic basic mathematical concepts, like integration, and be able to use them correctly to solve problems. Clearly, something was missing in my students’ understanding or perhaps memory that was causing them problems in this regard. In my initial work on knowledge transfer, I found that many students did not even recognize the need to transfer knowledge and for example, to integrate to solve a problem framed in an engineering context unless they were prompted to do so. Concerned by this troubling observation, coupled with my belief that engineers should be able to both understand and apply mathematical concepts in their coursework and careers, I determined to investigate the cause of the problem and, if possible, evidence a potential solution to help students transfer mathematical knowledge into an applied (engineering) context. In this study, I examine an expert (faculty) approach to problem solving using a semi-structured, think-aloud interview protocol coupled with a thorough thematic analysis for phenomenological themes. This analysis, grounded in an existing framework of knowledge transfer, provides a rich, thick description of the knowledge transfer, and problem solving process employed by the faculty expert and serves as a useful comparative case against which student approaches to problem solving and knowledge transfer can be judged. Important findings of this study relate to the extensive use of reflective and evaluative practices employed by the faculty member at all stages of the problem solving process. These internal checks and balances are rarely observed among novice problem solvers and perhaps represent behaviors that we, as educators, should seek to impart in our students if they are to become more adaptable engineers who are better equipped to transfer their knowledge and skills across a range of contexts. 

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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. Work in Progress: Promoting the Transfer of Math Skills to Engineering Statics

   https://doi.org/10.18260/1-2--44334  

   De_Rosa, Alexander; Reed, Teri; Arndt, Angela (June 2023, ASEE Conferences)

   Students often face difficulties in transferring concepts, knowledge and skills between their courses. This difficulty is especially true of the fundamental math and science courses that are often taught outside the major of the student and without engineering context. At the same time, graduating engineers are moving into an increasingly interdisciplinary workplace that values the ability to work broadly across a range of contexts. More work is needed to better prepare students to adapt their knowledge and skills to new situations and to demonstrate how the various courses and concepts within their curricula relate. In this study, we ask students, teaching assistants and faculty to “think aloud” through their solution to a statics problem that requires mathematical knowledge to be transferred in order to be solved. Two faculty, two teaching assistants and seven undergraduate students are interviewed as they think aloud through the problem. Interview transcripts and solutions to the statics problem are then examined for themes and patterns in responses in order to draw conclusions about the challenges different populations face in transferring knowledge and solving such problems. Observations indicated that students could apply simple integration skills to find the area of a shape when given a curve describing its shape, but could not use integration to find the centroid. The participants did however recall being taught how to calculate centroids in the past and discussed a lack of usage of this skill causing their inability to recall it correctly. Student participants in general displayed simple approaches to problem solving based on reading the problem statement rather than following an engineering approach starting with governing equations. A potential barrier to problem solving success was identified in the varying symbols used by different research participants which could lead to a lack of understanding if these symbols are not clearly explained and defined in a classroom setting. Future work will further examine these themes, as well as developing prompts and activities to promote knowledge transfer and problem solving success. 

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