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1. Oral exams for large-enrollment engineering courses to promote academic integrity and student engagement during remote instruction

   https://doi.org/10.1109/FIE49875.2021.9637124  

   Lubarda, M. ; Delson, N. ; Schurgers, C. ; Ghazinejad, M. ; Baghdadchi, S. ; Phan, A. ; Minnes, M. ; Relaford-Doyle, J. ; Klement, L. ; Sandoval, C ; et al ( October 2021 , 2021 IEEE Frontiers in Education Conference (FIE))

   This work-in-progress paper presents an innovative practice of using oral exams to maintain academic integrity and promote student engagement in large-enrollment engineering courses during remote instruction. With the abrupt and widespread transition to distance learning and assessment brought on by the COVID-19 pandemic, there has been a registered upsurge in academic integrity violations globally. To address the challenge of compromised integrity, in the winter quarter of 2021 we have implemented oral exams across six mostly high-enrollment mechanical and electrical engineering undergraduate courses. We present our oral exam design parameters in each of the courses and discuss how oral exams relate to academic integrity, student engagement, stress, and implicit bias. We also address the challenge of scalability, as most of our oral exams were implemented in large classes, where academic integrity and student-instructor disconnection have generally gotten disproportionately worse during remote learning. Our survey results indicate that oral exams have positively contributed to academic integrity in our courses. Based on our preliminary study and experiences, we expect oral exams can be effectively leveraged to hinder cheating and foster academic honesty in students, even when in-person instruction and assessment resumes.
2. Design Educators’ Conceptions of Prototyping in Engineering Design Courses

   Ali, Hadi ; Lande, Micah ( June 2019 , ASEE Annual Conference proceedings)

   This is a research study that investigates the range of conceptions of prototyping in engineering design courses through exploring the conceptions and implementations from the instructors’ perspective. Prototyping is certainly an activity central to engineering design. The context of prototyping to support engineering education and practice has a range of implementations in an undergraduate engineering curriculum, from first-year engineering to capstone engineering design experiences. Understanding faculty conceptions’ of the reason, purpose, and place of prototyping can help illustrate how teaching and learning of the engineering design process is realistically implemented across a curriculum and how students are prepared for work practice. We seek to understand, and consequently improve, engineering design teaching and learning, through transformations of practice that are based on engineering education research. In this exploratory study, we interviewed three faculty members who teach engineering design in project-based learning courses across the curriculum of an undergraduate engineering program. This builds on related work done by the authors that previously investigated undergraduate engineering students’ conceptions of prototyping activities and process. With our instructor participants, a similar interview protocol was followed through semi-structured qualitative interviews. Data analysis has been undertaken through an emerging thematic analysis of these interview transcripts. Early findingsmore »characterize the focus on teaching the design process; the kind of feedback that the educators provide on students’ prototypes; students’ behavior while working on design projects; and educators’ perspectives on the design course. Understanding faculty conceptions with students’ conceptions of prototyping can shed light on the efficacy of using prototyping as an authentic experience in design teaching and learning. In project-based learning courses, particular issues of authenticity and assessment are under consideration, especially across the curriculum. More specifically, “proportions of problems” inform “problem solving” as one of the key characteristics in design thinking, teaching and learning. More attention to prototyping as part of the study of problem-solving processes can be useful to enhance understanding of the impact of instructional design. Challenges for teaching engineering design exist, and may be due to difficulties in framing design problems, recognizing what expertise students possess, and assessing their expertise to help them reach their goals, all at an appropriate place and ambiguity with student learning goals. Initial findings show that prototyping activities can help students become more reflective on their design. Scaffolded activities in prototyping can support self-regulated learning by students. The range of support and facilities, such as campus makerspaces, may also help students and instructors alike develop industry-ready engineering students.« less
3. Where’s My Whiteboard? The Challenge of Moving Active-learning Mathematics Classes Online

   Nelson, Jill K. ; Rosenberg, Jessica ; Fernandez, Kathryn ; Shank, Julie ( July 2021 , 2021 ASEE Virtual Annual Conference)

   This research paper studies the challenges that mathematics faculty and graduate teaching assistants (GTAs) faced when moving active and collaborative calculus courses from in-person to virtual instruction. As part of a larger pedagogical change project (described below), the math department at a public Research-1 university began transitioning pre-calculus and calculus courses to an active and collaborative learning (ACL) format in Fall 2019. The change began with the introduction of collaborative worksheets in recitations which were led by GTAs and supported by undergraduate learning assistants (LAs). Students recitation periods collaboratively solving the worksheet problems on whiteboards. When COVID-19 forced the rapid transition to online teaching, these ACL efforts faced an array of challenges. Faculty and GTA reflections on the changes to teaching and learning provide insight into how instructional staff can be supported in implementing ACL across various modes of instruction. The calculus teaching change efforts discussed in this paper are part of an NSF-supported project that aims to make ACL the default method of instruction in highly enrolled gateway STEM courses across the institution. The theoretical framework for the project builds on existing work on grassroots change in higher education (Kezar and Lester, 2011) to study the effect of communitiesmore »of practice on changing teaching culture. The project uses course-based communities of practice (Wenger, 1999) that include instructors, GTAs, and LAs working together to design and enact teaching change in the targeted courses alongside ongoing professional development for GTAs and LAs. Six faculty and five GTAs involved in the teaching change effort in mathematics were interviewed after the Spring 2020 semester ended. Interview questions focused on faculty and GTA experiences implementing active learning after the rapid transition to online teaching. A grounded coding scheme was used to identify common themes in the challenges faced by instructors and GTAs as they moved online and in the impacts of technology, LA support, and the department community of practice on the move to online teaching. Technology, including both access and capabilities, emerged as a common barrier to student engagement. A particular barrier was students’ reluctance to share video or participate orally in sessions that were being recorded, making group work more difficult than it had been in a physical classroom. In addition, most students lacked access to a tablet for freehand writing, presenting a significant hurdle for sharing mathematical notation when physical whiteboards were no longer an option. These challenges point to the importance of incorporating flexibility in active learning implementation and in the professional development that supports teaching changes toward active learning, since what is conceived for a collaborative physical classroom may be implemented in a much different environment. The full paper will present a detailed analysis of the data to better understand how faculty and GTA experiences in the transition to online delivery can inform planning and professional development as the larger institutional change effort moves forward both in mathematics and in other STEM fields.« less
4. 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
5. 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