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  1. Hmelo-Silver, C. E. (Ed.)
    This paper develops a systematic approach to identifying and analyzing high school students’ debugging strategies when they work together to construct computational models of scientific processes in a block-based programming environment. We combine Markov models derived from students’ activity logs with epistemic network analysis of their collaborative discourse to interpret and analyze their model building and debugging processes. We present a contrasting case study that illustrates the differences in debugging strategies between two groups of students and its impact on their model-building effectiveness.
    Free, publicly-accessible full text available June 1, 2022
  2. 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
  3. 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
    Free, publicly-accessible full text available July 26, 2022
  4. The benefits of computational model building in STEM domains are well documented yet the synergistic learning processes that lead to the effective learning gains are not fully understood. In this paper, we analyze the discussions between students working collaboratively to build computational models to solve physics problems. From this collaborative discourse, we identify strategies that impact their model building and learning processes.
  5. Introducing computational modeling into STEM classrooms can provide opportunities for the simultaneous learning of computational thinking (CT) and STEM. This paper describes the C2STEM modeling environment for learning physics, and the processes students can apply to their learning and modeling tasks. We use an unsupervised learning method to characterize student learning behaviors and how these behaviors relate to learning gains in STEM and CT.
  6. The introduction of computational modeling into science curricula has been shown to benefit students’ learning, however the synergistic learning processes that contribute to these benefits are not fully understood. We study students’ synergistic learning of physics and computational thinking (CT) through their actions and collaborative discourse as they develop computational models in a visual block-structured environment. We adopt a case study approach to analyze students synergistic learning processes related to stopping conditions, initialization, and debugging episodes. Our findings show a pattern of evolving sophistication in synergistic reasoning for model-building activities.
  7. Computational modeling has been shown to benefit integrated learning of science and computational thinking (CT), however the mechanics of this synergistic learning are not well understood. In this research, we examine discourse during collaborative computational model building through the lens of a collaborative problem solving framework to gain insights into collaboration and synergistic learning of high school physics and CT. We pilot our novel approach in the context of C2STEM, a designed modeling environment, and examine collaboration and synergistic learning episodes in a video capture of a dyad modeling 2D motion with constant velocities. Our findings exhibit the promise ofmore »our approach and lay the foundation for guiding future automated approaches to detecting the synergistic learning of science and CT.« less
  8. Gresalfi, M. ; Horn, I. (Ed.)
    The design of most learning environments focuses on supporting students in making, constructing, and putting together projects on and off the screen, with much less attention paid to the many issues—problems, bugs, or traps—that students invariably encounter along the way. In this symposium, we present different theoretical and disciplinary perspectives on understanding how learners engage in debugging applications on and off screen, examine learners’ mindsets about debugging from middle school to college students and teachers, and present pedagogical approaches that promote strategies for debugging problems, even having learners themselves design problems for others. We contend that learning to identify andmore »fix problems—debug, troubleshoot, or get unstuck—in completing projects provides a productive space in which to explore multiple theoretical perspectives that can contribute to our understanding of learning and teaching critical strategies for dealing with challenges in learning activities and environments.« less
  9. Free, publicly-accessible full text available March 1, 2023
  10. Free, publicly-accessible full text available January 1, 2023