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1. 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
2. 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
3. Understanding Data Science Instruction in Multiple STEM Disciplines

   Snyder, C. ( January 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. Connecting Activity Implementation Characteristics to Student Buy-In Toward and Utilization of Formative Assessments Within Undergraduate Biology Courses

   https://doi.org/10.1007/s41979-021-00054-2  

   Brazeal, Kathleen R. ; Brown, Tanya L. ; Couch, Brian A. ( December 2021 , Journal for STEM Education Research)

   Abstract While formative assessments (FAs) can facilitate learning within undergraduate STEM courses, their impact likely depends on many factors, including how instructors implement them, whether students buy-in to them, and how students utilize them. FAs have many different implementation characteristics, including what kinds of questions are asked, whether questions are asked before or after covering the material in class, how feedback is provided, how students are graded, and other logistical considerations. We conducted 38 semi-structured interviews with students from eight undergraduate biology courses to explore how various implementation characteristics of in-class and out-of-class FAs can influence student perceptions and behaviors.more »We also interviewed course instructors to provide context for understanding student experiences. Using thematic analysis, we outlined various FA implementation characteristics, characterized the range of FA utilization behaviors reported by students, and identified emergent themes regarding the impact of certain implementation characteristics on student buy-in and utilization. Furthermore, we found that implementation characteristics have combined effects on student engagement and that students will tolerate a degree of “acceptable discomfort” with implementation features that contradict their learning preferences. These results can aid instructor reflection and guide future research on the complex connections between activity implementation and student engagement within STEM disciplines.« less
5. Strengthening Community College Engineering Programs through Alternative Learning Strategies: Developing Resources for Flexible Delivery of a Materials Science Course

   Dunmire, Erik ; Enriquez, Amelito ; Rebold, Thomas ; Schiorring, Eva ; Langhoff, Nicholas ; Huang, Tracy ( April 2017 , 2017 ASEE Pacific Southwest Section Conference)

   Community colleges provide an important pathway for many prospective engineering graduates, especially those from traditionally underrepresented groups. However, due to a lack of facilities, resources, student demand and/or local faculty expertise, the breadth and frequency of engineering course offerings is severely restricted at many community colleges. This in turn presents challenges for students trying to maximize their transfer eligibility and preparedness. Through a grant from the National Science Foundation Improving Undergraduate STEM Education program (NSF IUSE), three community colleges from Northern California collaborated to increase the availability and accessibility of a comprehensive lower-division engineering curriculum, even at small-to-medium sized communitymore »colleges. This was accomplished by developing resources and teaching strategies that could be employed in a variety of delivery formats (e.g., fully online, online/hybrid, flipped face-to-face, etc.), providing flexibility for local community colleges to leverage according to their individual needs. This paper focuses on the iterative development, testing, and refining of the resources for an introductory Materials Science course with 3-unit lecture and 1-unit laboratory components. This course is required as part of recently adopted statewide model associate degree curricula for transfer into Civil, Mechanical, Aerospace, and Manufacturing engineering bachelor’s degree programs at California State Universities. However, offering such a course is particularly challenging for many community colleges, because of a lack of adequate expertise and/or laboratory facilities and equipment. Consequently, course resources were developed to help mitigate these challenges by streamlining preparation for instructors new to teaching the course, as well as minimizing the face-to-face use of traditional materials testing equipment in the laboratory portion of the course. These same resources can be used to support online hybrid and other alternative (e.g., emporium) delivery approaches. After initial pilot implementation of the course during the Spring 2015 semester by the curriculum designer in a flipped student-centered format, these same resources were then implemented by an instructor who had never previously taught the course, at a different community college that did not have its own materials laboratory facilities. A single site visit was arranged with a nearby community college to afford students an opportunity to complete certain lab activities using traditional materials testing equipment. Lessons learned during this attempt were used to inform curriculum revisions, which were evaluated in a repeat offering the following year. In all implementations of the course, student surveys and interviews were used to determine students’ perceptions of the effectiveness of the course resources, student use of these resources, and overall satisfaction with the course. Additionally, student performance on objective assessments was compared with that of traditional lecture delivery of the course by the curriculum designer in prior years. During initial implementations of the course, results from these surveys and assessments revealed low levels of student satisfaction with certain aspects of the flipped approach and course resources, as well as reduced learning among students at the alternate institution. Subsequent modifications to the curriculum and delivery approach were successful in addressing most of these deficiencies.« less