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1. Progress in the Nationwide Dissemination and Assessment of Low-Cost Desktop Learning Modules and Adaptation of Pedagogy to a Virtual Era

   Van Wie, BJ ( July 2021 , American Society for Engineering Education)

   The development of tools that promote active learning in engineering disciplines is critical. It is widely understood that students engaged in active learning environments outperform those taught using passive methods. Previously, we reported on the development and implementation of hands-on Low-Cost Desktop Learning Modules (LCDLMs) that replicate real-world industrial equipment which serves to create active learning environments. Thus far, miniaturized venturi meter, hydraulic loss, and double-pipe and shell & tube heat exchanger DLMs have been utilized by hundreds of students across the country. It was demonstrated that the use of DLMs in face-to-face classrooms results in statistically significant improvements inmore »student performance as well as increases in student motivation compared to students taught in a traditional lecture-only style classroom. Last year, participants in the project conducted 45 implementations including over 600 DLMs at 24 universities across the country reaching more than 1,000 students. In this project, we report on the significant progress made in broad dissemination of DLMs and accompanying pedagogy. We demonstrate that DLMs serve to increase student learning gains not only in face-to-face environments but also in virtual learning environments. Instructional videos were developed to aid in DLM-based learning during the COVID-19 pandemic when instructors were limited to virtual instruction. Preliminary results from this work show that students working with DLMs even in a virtual setting significantly outperform those taught without DLM-associated materials. Significant progress has also been made on the development of a new DLM cartridge: a see-through 3D-printed miniature fluidized bed. The new 3D printing methodology will allow for rapid prototyping and streamlined development of DLMs. A 3D-printed evaporative cooling tower DLM will also be developed in the coming year. In October 2020, the team held a virtual implementers workshop to train new participating faculty in DLM use and implementation. In total, 13 new faculty participants from 10 universities attended the 6-hour, 2-day workshop and plan to implement DLMs in their classrooms during this academic year. In the last year, this project was disseminated in 8 presentations at the American Society for Engineering Education (ASEE) Virtual Conference (June 2020) and American Institute of Chemical Engineers Annual Conference (November 2019) as well as the AIChE virtual Community of Practice Labs Group and a seminar at a major university, ultimately disseminating DLM pedagogy to approximately 200 individuals including approximately 120 university faculty. Further, the former group postdoc has accepted an instructor faculty position at University of Wisconsin Madison where she will teach unit operations among other subjects; she and the remainder of the team believe the LCDLM project has prepared her well for that position. In the remaining 2.5 years of the project, we will continue to evaluate the effectiveness of DLMs in teaching key heat transfer and fluid dynamics concepts thru implementations in the rapidly expanding pool of participating universities. Further, we continue our ongoing efforts in creating the robust support structure necessary for large-scale adoption of hands-on educational tools for promotion of hands-on interactive student learning.« less
2. Progress in the Nationwide Dissemination and Assessment of Low-Cost Desktop Learning Modules and Adaptation of Pedagogy to a Virtual Era

   Van Wie, Bernard ; Bryant, Kristin ; Kaiphanliam, Kitana ; Khan, Aminul ; Olufunso, Oje ; Dutta, Prashanta ; Gartner, Jacqueline ; Thiessen, David ( July 2021 , ASEE Annual Conference proceedings)

   The development of tools that promote active learning in engineering disciplines is critical. It is widely understood that students engaged in active learning environments outperform those taught using passive methods. Previously, we reported on the development and implementation of hands-on Low-Cost Desktop Learning Modules (LCDLMs) that replicate real-world industrial equipment which serves to create active learning environments. Thus far, miniaturized venturi meter, hydraulic loss, and double-pipe and shell & tube heat exchanger DLMs have been utilized by hundreds of students across the country. It was demonstrated that the use of DLMs in face-to-face classrooms results in statistically significant improvements inmore »student performance as well as increases in student motivation compared to students taught in a traditional lecture-only style classroom. Last year, participants in the project conducted 45 implementations including over 600 DLMs at 24 universities across the country reaching more than 1,000 students. In this project, we report on the significant progress made in broad dissemination of DLMs and accompanying pedagogy. We demonstrate that DLMs serve to increase student learning gains not only in face-toface environments but also in virtual learning environments. Instructional videos were developed to aid in DLM-based learning during the COVID-19 pandemic when instructors were limited to virtual instruction. Preliminary results from this work show that students working with DLMs even in a virtual setting significantly outperform those taught without DLM-associated materials. Significant progress has also been made on the development of a new DLM cartridge: a see-through 3Dprinted miniature fluidized bed. The new 3D printing methodology will allow for rapid prototyping and streamlined development of DLMs. A 3D-printed evaporative cooling tower DLM will also be developed in the coming year. In October 2020, the team held a virtual implementers workshop to train new participating faculty in DLM use and implementation. In total, 13 new faculty participants from 10 universities attended the 6-hour, 2- day workshop and plan to implement DLMs in their classrooms during this academic year. In the last year, this project was disseminated in 8 presentations at the ASEE Virtual Conference (June 2020) and American Institute of Chemical Engineers Annual Conference (November 2019) as well as the AIChE virtual Community of Practice Labs Group and a seminar at a major university, ultimately disseminating DLM pedagogy to approximately 200 individuals including approximately 120 university faculty. Further, the former group postdoc has accepted an instructor faculty position at University of Wisconsin Madison where she will teach unit operations among other subjects; she and the remainder of the team believe the LCDLM project has prepared her well for that position. In the remaining 2.5 years of the project, we will continue to evaluate the effectiveness of DLMs in teaching key heat transfer and fluid dynamics concepts thru implementations in the rapidly expanding pool of participating universities. Further, we continue our ongoing efforts in creating the robust support structure necessary for large-scale adoption of hands-on educational tools for promotion of hands-on interactive student learning.« 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