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1. What do Undergraduate Engineering Students and Pre-service Teachers Learn by Collaborating and Teaching Engineering and Coding Through Robotics?

   Kidd, J. ; Kaipa, K. ; Sacks, S. ; Ringleb, S. ; Pazos, P. ; Gutierrez, K. ; Ayala, O. M. ; de Souza Almeida, L. M. ( June 2020 , 2020 ASEE Virtual Annual Conference Content Access, Virtual Online)

   This research paper presents preliminary results of an NSF-supported interdisciplinary collaboration between undergraduate engineering students and preservice teachers. The fields of engineering and elementary education share similar challenges when it comes to preparing undergraduate students for the new demands they will encounter in their profession. Engineering students need interprofessional skills that will help them value and negotiate the contributions of various disciplines while working on problems that require a multidisciplinary approach. Increasingly, the solutions to today's complex problems must integrate knowledge and practices from multiple disciplines and engineers must be able to recognize when expertise from outside their field canmore »enhance their perspective and ability to develop innovative solutions. However, research suggests that it is challenging even for professional engineers to understand the roles, responsibilities, and integration of various disciplines, and engineering curricula have traditionally left little room for development of non-technical skills such as effective communication with a range of audiences and an ability to collaborate in multidisciplinary teams. Meanwhile, preservice teachers need new technical knowledge and skills that go beyond traditional core content knowledge, as they are now expected to embed engineering into science and coding concepts into traditional subject areas. There are nationwide calls to integrate engineering and coding into PreK-6 education as part of a larger campaign to attract more students to STEM disciplines and to increase exposure for girls and minority students who remain significantly underrepresented in engineering and computer science. Accordingly, schools need teachers who have not only the knowledge and skills to integrate these topics into mainstream subjects, but also the intention to do so. However, research suggests that preservice teachers do not feel academically prepared and confident enough to teach engineering-related topics. This interdisciplinary project provided engineering students with an opportunity to develop interprofessional skills as well as to reinforce their technical knowledge, while preservice teachers had the opportunity to be exposed to engineering content, more specifically coding, and develop competence for their future teaching careers. Undergraduate engineering students enrolled in a computational methods course and preservice teachers enrolled in an educational technology course partnered to plan and deliver robotics lessons to fifth and sixth graders. This paper reports on the effects of this collaboration on twenty engineering students and eight preservice teachers. T-tests were used to compare participants’ pre-/post- scores on a coding quiz. A post-lesson written reflection asked the undergraduate students to describe their robotics lessons and what they learned from interacting with their cross disciplinary peers and the fifth/sixth graders. Content analysis was used to identify emergent themes. Engineering students’ perceptions were generally positive, recounting enjoyment interacting with elementary students and gaining communication skills from collaborating with non-technical partners. Preservice teachers demonstrated gains in their technical knowledge as measured by the coding quiz, but reported lacking the confidence to teach coding and robotics independently of their partner engineering students. Both groups reported gaining new perspectives from working in interdisciplinary teams and seeing benefits for the fifth and sixth grade participants, including exposing girls and students of color to engineering and computing.« less
2. Results of an Intro to Mechanics Course Designed to Support Student Success in Physics I and Foundational Engineering Courses

   Menezes, Gustavo B. ; Nerenberg, Paul S. ; Li, Ni ; Allen, Emily L. ( June 2020 , ASEE Annual Conference Proceedings)

   This complete evidence-based practice paper discusses the strategies and results of an introduction to mechanics course, designed to prepare students for introductory-level physics and other fundamental courses in engineering, such as statics, strength of materials, and dynamics. The course was developed to address historically high failure (DFW) rates in the physics courses and is part of a set of interventions implemented to support student success in a college of engineering and computer science. The course focuses on providing in-depth understanding of Newton’s Laws of motion, free-body diagrams, and linear and projectile motion. Because it focuses on a limited number ofmore »competencies, it is possible to spend more time on inquiry-based activities and in-class discussions. The course framework was designed considering the Ebbinghaus’ Forgetting Curve, to provide students with learning opportunities in 6-day cycles: (i) day 1: a pre-class learning activity (reading or video) and a quiz; (ii) day 2: in-class Kahoot low-stakes quiz with discussion, a short lecture with embedded time for problem-solving and discussion, and in-class activities (labs, group projects); (iii) day 4: homework due two days after the class; (iv) day 6: homework self-reflection (autopsy based on provided solutions) two days after homework is due. The assessment of course performance is based on the well-characterized force concept inventory (FCI) exam that is administered before the intro to mechanics course and both before and after the Physics I course; and on student performance (grades) in Physics and Statics courses. Results from the FCI pre-test show that students who took the introduction to mechanics course (treatment group) started the physics course with a much better understanding of force concepts than other students in the course. The FCI post-test shows better normalized gain for the treatment group, compared to other students, which is also aligned with student performance in the course. Additionally, student performance is significantly better in statics, with 25% DWF rate compared to 50% for the other students. In summary, the framework of the course, which focuses on providing students with in-depth understanding of force concepts, has led to better learning and performance in Physics I, but importantly it has also helped students achieve better performance in the Statics course, the first fundamental course in civil and mechanical engineering programs.« less
3. Understanding Data Science Instruction in Multiple STEM Disciplines

   Snyder, C. ; Asamen, D. M. ; Naseri, M. Y. ; Aryal, N. ; Biswas, G. ; Dubey, A. ; Henrick, E. ; Hotchkiss, E. R. ; Jha, M. K. ; Jiang, S. X. ; et al ( July 2021 , 2021 ASEE Virtual Annual Conference)

   As technology advances, data driven work is becoming increasingly important across all disciplines. Data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to “benefit from a fundamental awareness of and competence in data science”[9]. This project has formed a research practice partnership that brings together STEM+C instructors and researchers from three universities and an education research and consulting group. We aim to use high frequency monitoring data collected frommore »real-world systems to develop and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of data science curricula that are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students’ year of study, students’ reasons for taking class, and students’ background expertise and knowledge. These findings indicate the challenges in developing data modules for multidisciplinary use. We hope that the analysis and reflections on our initial offerings has improved our understanding of these challenges, and how we may address them when designing future data science teaching modules. These are the first steps in a design-based approach to developing data science modules that may be offered across multiple courses.« less
4. Understanding Data Science Instruction in Multiple STEM Domains

   Snyder, Caitlin ( July 2021 , 2021 ASEE Virtual Annual Conference Content Access.)

   As technology advances, data-driven work is becoming increasingly important across all disciplines. Data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to “benefit from a fundamental awareness of and competence in data science”[9]. This project has formed a research-practice partnership that brings together STEM+C instructors and researchers from three universities and an education research and consulting group. We aim to use high-frequency monitoring data collected from real-world systems tomore »develop and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of data science curricula that are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students’ year of study, students’ reasons for taking class, and students’ background expertise and knowledge. These findings indicate the challenges in developing data modules for multidisciplinary use. We hope that the analysis and reflections on our initial offerings have improved our understanding of these challenges, and how we may address them when designing future data science teaching modules. These are the first steps in a design-based approach to developing data science modules that may be offered across multiple courses.« less
5. Understanding Data Science Instruction in Multiple STEM Domains

   Snyder, Caitlin ( July 2021 , 2021 ASEE Virtual Annual Conference)

   As technology advances, data-driven work is becoming increasingly important across all disciplines. Data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to “benefit from a fundamental awareness of and competence in data science”[9]. This project has formed a research-practice partnership that brings together STEM+C instructors and researchers from three universities and education research and consulting groups. We aim to use high-frequency monitoring data collected from real-world systems to developmore »and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of data science curricula that are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students’ year of study, students’ reasons for taking class, and students’ background expertise and knowledge. These findings indicate the challenges in developing data modules for multidisciplinary use. We hope that the analysis and reflections on our initial offerings have improved our understanding of these challenges, and how we may address them when designing future data science teaching modules. These are the first steps in a design-based approach to developing data science modules that may be offered across multiple courses.« less