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1. 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
2. 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
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. Learner Analytics in Engineering Education: A Detailed Account of Practices Used in the Cleaning and Manipulation of Learning Management System Data from Online Undergraduate Engineering Courses

   Kittur, J. ; Bekki, J. ; Brunhaver, S. ( January 2020 , Proceedings of the American Society for Engineering Education)

   This work falls under the evidence-based practice type of paper. Online undergraduate engineering education is rapidly increasing in use. The online format not only provides greater flexibility and ease of access for students, but also has lower costs for universities when compared to face-to-face courses. Even with these generally positive attributes, online courses face challenges with respect to student attrition. Numerous studies have shown that the dropout rate in online courses is higher than that for in-person courses, and topics related to online student persistence remain of interest. Data describing student interactions with their Learning Management System (LMS) provide anmore »important lens through which online student engagement and corresponding persistence decisions can be studied, but many engineering education researchers may lack experience in working with LMS interaction data. The purpose of this paper is to provide a concrete example for other engineering education researchers of how LMS interaction data from online undergraduate engineering courses can be prepared for analysis. The work presented here is part of a larger National Science Foundation-funded study dedicated to developing a theoretical model for online undergraduate engineering student persistence based on student LMS interaction activities and patterns. Our sample dataset includes six courses, two from electrical engineering and four from engineering management, offered during the fall 2018 semester at a large, public southwestern university. The LMS interaction data provides details about students’ navigations to and submissions of different course elements including quizzes, assignments, discussion forums, wiki pages, attachments, modules, the syllabus, the gradebook, and course announcements. Relatedly, the features created from the data in this study can be classified into three categories: 1) learning page views, which capture student interactions with course content, 2) procedural page views, which capture student navigation to course management activities, and 3) social page views, which capture learner-to-learner and learner-to-instructor interactions. The full paper will provide the rationale and details involved in choices related to data cleaning, manipulation, and feature creation. A complete list of features will also be included. These features will ultimately be combined with associative classification to discover relationships between student-LMS interactions and persistence decisions.« 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