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1. “It let me merge my love of teaching with research”: A qualitative investigation of the career pathways of biology education researchers

   https://doi.org/10.1371/journal.pone.0312243  

   Driessen, Emily P; Steele, Ariel L; Costello, Robin A; Brewer, Peyton; Ballen, Cissy J (October 2024, PLOS ONE)

   Malele-Kolisa, Yolanda (Ed.)

   Discipline-based education research—a field of research that investigates teaching and learning within STEM disciplines—has emerged over the last few decades to improve the quality of STEM education worldwide. Simple qualitative questions concerning the career backgrounds and motivations of the individuals who conduct this research have yet to be explored. Here, we surveyed and interviewed discipline-based education researchers about their career trajectories and motivations to pursue this field of research. We focused specifically on recruiting biology education research faculty members at colleges and universities. We used the Social Influence Model and Social Cognitive Career Theory to develop and analyze survey and semi-structured interview questions. Findings revealed participant career paths all began with disciplinary undergraduate and graduate-level biology education. We noticed participants began conducting biology education research due to theirvaluesandpersonal interests, while additionally being swayed bycontextual factors. Specifically, participantsvaluedbiology education research because it allowed them to make a difference in the world and provided them with a community open to change and collaboration. Biology education research allowed them to explore theirinterestsin teaching and evidence-based approaches to education. Thesevaluesandinterestswere impacted bycontextual factors, including discoveries of opportunities, positive (or negative) experiences with mentorship, exposure to evidence-based teaching literature, considerations of salary and job security, and experiences with gender-based discrimination. Our results underscore the importance of harnessing individual values and interests—especially those centered on evidence-based teaching practices and making a difference in the world—while fostering a positive and supportive academic environment. This research reveals pathways toward discipline-based education research careers. Additionally, this research can inform the development of graduate programs and funding opportunities. 

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2. Using an engagement lens to model active learning in the geosciences

   https://doi.org/10.1080/10899995.2021.1913715  

   LaDue, N. D.; McNeal, P. M.; Ryker, K.; St. John, K.; van der Hoeven Kraft, K. J. (April 2021, Journal of Geoscience Education)

   Hannula, K. (Ed.)

   Active learning research emerged from the undergraduate STEM education communities of practice, some of whom identify as discipline-based education researchers (DBER). Consequently, current frameworks of active learning are largely inductive and based on emergent patterns observed in undergraduate teaching and learning. Alternatively, classic learning theories historically originate from the educational psychology community, which often takes a theory-driven, or deductive research approach. The broader transdisciplinary education research community is now struggling to reconcile the two. That is, how is a theory of active learning distinct from other theories of knowledge construction? We discuss the underpinnings of active learning in the geosciences, drawing upon extant literature from the educational psychology community on engagement. Based on Sinatra et al. engagement framework, we propose a model for active learning in the geosciences with four dimensions: behavioral, emotional, cognitive, and agentic. We then connect existing literature from the geoscience education community to the model to demonstrate the current gaps in our literature base and opportunities to move the active learning geoscience education research (GER) forward. We propose the following recommendations for future investigation of active learning in the geosciences: (1) connect future GER to our model of active learning in the geosciences, (2) measure more than content learning, (3) document research methods and outcomes with effect sizes to accumulate evidence, and (4) prioritize research on dimensions of active learning essential to the geosciences. 

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3. Learning Trajectories through Undergraduate Engineering Curricula and Experiences

   https://doi.org/10.18260/1-2--34905  

   Lande, Micah (June 2020, 2020 ASEE Virtual Annual Conference)

   null (Ed.)

   This NSF EAGER research paper investigates how undergraduate STEM and engineering students’ learning trajectories evolve over time, from 1st to senior year, along a novice to expert spectrum. We borrow the idea of “learning trajectories” from mathematics education that can paint the evolution of students’ knowledge and skills over time over a set of learning experiences. Curricula for undergraduate engineering programs can reflect an intended pathway of knowledge construction within a discipline. We intend our study of individual students within undergraduate STEM and engineering programs can highlight how this may happen in situ and how it may be similar or might differ from a given, prescribed programs of study among disciplines. We use a theoretical framework based in adaptive expertise and design thinking adaptive expertise to develop a design learning continuum further. Envisioned routes through disciplinary undergraduate curricula and student conceptions of their design process are explored through qualitative, semi-structured interviews with undergraduate 1st year and senior year students across STEM, engineering and non-STEM field such as computer science, mechanical engineering, general engineering, mathematics, science, English, and art. We also conduct similar interviews with faculty in these fields who are responsible and knowledgeable for undergraduate programs about their perceived benefits for the structure of their program’s curriculum. Additional information is collected from noticing the organizational and pedagogical structures of the relative undergraduate curriculum. Initial findings/outcomes suggest that traditions to knowledge construction both differ across disciplinary approaches and have similarities across non-obvious disciplinary relationships. Faculty have a firm understanding of how one class chains from one to another; students have less of a field of view for how mindful chunks of knowledge combine together. 

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4. Predicting implementation of active learning by tenure-track teaching faculty using robust cluster analysis

   https://doi.org/10.1186/s40594-022-00365-9  

   Denaro, Kameryn; Kranzfelder, Petra; Owens, Melinda T.; Sato, Brian; Zuckerman, Austin L.; Hardesty, Rebecca A.; Signorini, Adriana; Aebersold, Andrea; Verma, Mayank; Lo, Stanley M. (July 2022, International Journal of STEM Education)

   Abstract BackgroundThe University of California system has a novel tenure-track education-focused faculty position called Lecturer with Security of Employment (working titles: Teaching Professor or Professor of Teaching). We focus on the potential difference in implementation of active-learning strategies by faculty type, including tenure-track education-focused faculty, tenure-track research-focused faculty, and non-tenure-track lecturers. In addition, we consider other instructor characteristics (faculty rank, years of teaching, and gender) and classroom characteristics (campus, discipline, and class size). We use a robust clustering algorithm to determine the number of clusters, identify instructors using active learning, and to understand the instructor and classroom characteristics in relation to the adoption of active-learning strategies. ResultsWe observed 125 science, technology, engineering, and mathematics (STEM) undergraduate courses at three University of California campuses using the Classroom Observation Protocol for Undergraduate STEM to examine active-learning strategies implemented in the classroom. Tenure-track education-focused faculty are more likely to teach with active-learning strategies compared to tenure-track research-focused faculty. Instructor and classroom characteristics that are also related to active learning include campus, discipline, and class size. The campus with initiatives and programs to support undergraduate STEM education is more likely to have instructors who adopt active-learning strategies. There is no difference in instructors in the Biological Sciences, Engineering, or Information and Computer Sciences disciplines who teach actively. However, instructors in the Physical Sciences are less likely to teach actively. Smaller class sizes also tend to have instructors who teach more actively. ConclusionsThe novel tenure-track education-focused faculty position within the University of California system represents a formal structure that results in higher adoption of active-learning strategies in undergraduate STEM education. Campus context and evolving expectations of the position (faculty rank) contribute to the symbols related to learning and teaching that correlate with differential implementation of active learning. 

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5. 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. 

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