More Like this

1. 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
2. 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
3. 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
4. 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
5. Preservice elementary teachers' perceptions of their science laboratory instructors in a phenomena‐based laboratory and how it impacts their conceptual development

   https://doi.org/10.1002/tea.21926  

   Sangha, Alvir S. ; Donnelly‐Hermosillo, Dermot F. ; Nelson, Frederick P. ( February 2024 , Journal of Research in Science Teaching)

   Phenomena‐based approaches have become popular for elementary school teachers to engage children's innate curiosity in the natural world. However, integrating such phenomena‐based approaches in existing science courses within teacher education programs present potential challenges for both preservice elementary teachers (PSETs) and for laboratory instructors, both of whom may have had limited opportunities to learn or teach science within the student and instructor roles inherent within these approaches. This study uses a convergent parallel mixed‐methods approach to investigate PSETs' perceptions of their laboratory instructor's role within a Physical Science phenomena‐based laboratory curriculum and how it impacts their conceptual development (2 instructors/121 students). We also examine how the two laboratory instructors' discursive moves within the laboratory align with their's and PSETs' perceptions of the instructor role. Qualitative data includes triangulation between a student questionnaire, an instructor questionnaire, and video classroom observations, while quantitative data includes a nine‐item open response pre‐/post‐semester conceptual test. Guided by Mortimer's and Scott's analytic framework, our findings show that students primarily perceive their instructors as a guide/facilitator or an authoritarian/evaluator. Using Linn's knowledge integration framework, analysis of pre‐/post‐tests indicates that student outcomes align with students' perceptions of their instructors, with students who perceive their instructor as a guide/facilitator having significantly better pre‐/post‐outcomes. Additional analysis of scientific discourse from the classroom observations illustrates how one instructor primarily supports PSETs' perspectives on authentic science learning through dialogic–interactive talk moves whereas the other instructor epistemologically stifles personally relevant investigations with authoritative–interactive or authoritative–noninteractive discourse moves. Overall, this study concludes by discussing challenges facing laboratory instructors that need careful consideration for phenomena‐based approaches. 

   more » « less