More Like this

1. Work in Progress: Creating Alternative Learning Strategies for Transfer Engineering Programs

   https://doi.org/10.18260/p.25085  

   Enriquez, Amelito ; Dunmire, Erik ; Rebold, Tom ; Langhoff, Nick ; Schiorring, Eva ( June 2015 , 2015 ASEE Annual Conference & Exposition)

   The 2012 President’s Council of Advisors on Science and Technology (PCAST) report “Engage to Excel: Producing One Million Additional College Graduates with Degrees in Science,Technology, Engineering, and Mathematics” indicated that addressing the retention problem in the first two years of college is the most promising and cost-effective strategy to produce the STEM professionals needed in order to retain US historical preeminence in science and technology. The California Community College System, with its 112 community colleges and 71 off-campus centers enrolling approximately 2.3 million students (roughly a third of all US community college students) is in a prime position to grow the future STEM workforce.However, in the face of shrinking resources and increasing costs and other barriers, an effective approach is needed in order to capitalize on this opportunity. One prong in this approach is to more fully exploit modern technological capabilities to reduce costs, broaden access, and improve educational productivity. This paper presents preliminary results of a collaborative project, Creating Alternative Learning Strategies for Transfer Engineering Programs (CALSTEP), which aims to strengthen community college engineering programs using distance education and other alternative delivery strategies that will enable small-to-medium community college engineering programs to provide their students access to lower-division engineering courses needed to be competitive for transfer to four-year engineering programs. Funded by a three-year grant through the National Science Foundation Improving Undergraduate STEM Education (NSF IUSE) program, CALSTEP will leverage existing educational resources and develop new ones for online lecture courses, as well as core engineering laboratory courses that are delivered either completely online, or with limited face-to-face interactions. The initial areas of focus for laboratory course development are: Introduction to Engineering, Engineering Graphics, Materials Science, and Circuit Analysis. CALSTEP will also develop alternative models of flipped classroom instruction to improve student success and enhance student access to engineering courses that otherwise could not be supported in traditional delivery modes due to low enrollment. The project will iteratively evaluate and refine the curriculum over the three-year grant period, as well as train other community college engineering faculty in the effective use of the curriculum and resources developed. 

   more » « less
2. Understanding Data Science Instruction in Multiple STEM Disciplines

   Snyder, C. ( January 2021 , 2021 ASEE Virtual Annual Conference)

   null (Ed.)

   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 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. 

   more » « 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)

   null (Ed.)

   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 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. 

   more » « less
4. Understanding Data Science Instruction in Multiple STEM Domains

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

   null (Ed.)

   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 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. 

   more » « less
5. Understanding Data Science Instruction in Multiple STEM Domains

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

   null (Ed.)

   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 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 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. 

   more » « less