This National Science Foundation Improving Undergraduate STEM Education funded project integrated data science into six undergraduate STEM courses at a public university, a private university, and a Historically Black College and University. A research–practice partnership involving faculty, graduate students, and an evaluation team developed 12 discipline-specific data science modules that were implemented multiple times and reached over 1,000 students. The modules used real-world datasets, including environmental and traffic data, to embed data science concepts into existing STEM courses and better prepare students for data-driven careers.

Using a mixed methods approach, the team collected instructor interviews, student surveys, pre and post data, and assessment results. Instructors valued the flexibility to tailor content, emphasizing hands-on learning, data visualization, and statistical analysis. Challenges included varied student backgrounds and adjustments during COVID-19.

Students reported increased interest, confidence, and skills in data science after completing the modules. They appreciated real-world applications but requested more guidance and time for complex tools and topics. Overall, the project demonstrates that embedding discipline-specific data science into existing STEM courses is an effective and sustainable way to strengthen data literacy.

Data science skills are increasingly important for undergraduate students in engineering and science, but it can be challenging to integrate these skills effectively into existing courses. This study examined the impact of embedding discipline-specific data science modules into undergraduate STEM courses at three universities in the United States, using a collaborative research-practice approach. Researchers analyzed survey responses from 877 students, instructor grades, and interviews across six courses to understand changes in student perceptions of data science across different demographics, academic levels, and disciplines, as well as how students’ self-assessments compared with instructor evaluations.

The results showed that students’ confidence and perception of their data science abilities improved significantly after completing one or more modules, regardless of the course or university. Student self-assessments closely matched instructor evaluations, indicating consistent recognition of skill development. Students reported benefits such as connecting data science to real-world applications and career relevance, though they also noted challenges with learning new data analysis tools and navigating varying prior experience levels. These findings provide valuable guidance for educators seeking to integrate data science into STEM curricula in ways that are meaningful, engaging, and practical.

Figure 1.ÌýResults of the mixed-effects models indicating the effect of taking one or more modules on constructs. Adj. Diff indicates the adjusted difference (i.e. difference after controlling for course and institutions) between pre- and post-survey scores, Eff. Size the effect size calculated using Westfall, Kenny, and Judd () method,ÌýZÌýthe test statistic,ÌýPÌýtheÌýp-value, andÌýNÌýthe sample size. Blue and orange dots represent pre- and post-survey mean adjusted scores, respectively, with horizontal red lines indicating 95% confidence intervals.

Researchers in learning sciences have suggested that there is a strong connection between learning STEM subjects (science, technology, engineering, and math) and developing computational thinking (CT) skills. Studies have shown that CT and STEM learning support each other, but not enough work has been done to clearly define the core CT knowledge and skills that should be taught in K-12 schools to improve both STEM and CT learning. As a result, many important CT concepts are often missing or underrepresented in classrooms for children from kindergarten through 12th grade.

We believe that CT ideas and practices are not only essential for computer science education but also help students build important modeling and problem-solving skills that apply to STEM subjects. In this chapter, we build on our previous framework to promote combined learning of science content and CT skills in middle school classrooms. We explain the main STEM and CT ideas and practices that we have introduced into science classes through our specially designed lessons. We also describe CTSiM, a computer-based learning tool created in our lab, which helps students learn these skills.

We share results from studies in middle school classrooms using CTSiM, showing that students made strong progress in both science and computational thinking concepts. Our assessments also help us understand how students learn and practice these skills, revealing important connections between their learning behaviors and their growth in STEM and CT knowledge.

Fig.Ìý12.1

The STEM + CT framework

Computational models (CMs) give pre-college students a chance to connect science, technology, engineering, and math (STEM) with computational thinking (CT) in ways that reflect how STEM is used in the real world. But not all students and teachers are ready to take on the challenge of learning or teaching programming. To make computing more accessible and reduce the difficulty of learning to code, this study explores simpler versions of computational modeling that involve little or no programming. Instead of writing code, students focus on understanding and evaluating existing code and computer simulations. The paper shares results from a small pilot study that looked at how students engaged with CT practices and what challenges they faced across three types of these easier-to-use CM activities.Ìý

Getting students involved in computational modeling (CM)—where they use computer-based tools to explain scientific processes—can help them get ready for STEM careers and make it easier to introduce computer science in pre-college classrooms. This study looks at the need for short and manageable CM lessons that regular teachers can use in science classes, even with students who’ve never done any coding and come from groups historically underrepresented in STEM. The researchers designed and tested a one-week CM unit and looked at how well it worked in classrooms, what students learned, and how they engaged with the modeling activities. Students showed significant improvements in their understanding after the unit, even if they had already studied motion (kinematics) before. Two examples show how students used CM to learn both physics and computational thinking.Ìý

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This paper looks at how middle school STEM teachers understand what their students are learning and how they solve problems during a problem-based learning unit. It also explores how teachers use this understanding to make changes to their lesson plans based on evidence from their students’ work. By analyzing teachers’ spoken thoughts while they think through these tasks, the study identifies key connections that help teachers move from interpreting student learning to actually changing their teaching. The paper compares the approaches of an experienced teacher and a novice teacher, and discusses how these findings can help improve professional training for STEM teachers and the development of tools to support them.Ìý

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Recently, there has been a big increase in creating lessons and tools that combine computing (C) with Science, Technology, Engineering, and Math (STEM) programs. These learning environments encourage real-world problem-solving while helping students learn STEM and computing skills at the same time. In this study, we looked at how students worked in pairs to build computer models that show how objects move (called computational kinematics models). We developed a specific way to measure, based on their conversations, how well students combined science and computing ideas while solving problems. We also measured social aspects like fairness and how students took turns talking during their discussions.Ìý

We observed and described how students planned, carried out, checked, and thought about their work while building these models together. The study explores how students’ teamwork behaviors affect how well they do on these STEM+C modeling tasks. By looking at the connections between group teamwork, turn-taking, and fairness with how well the students completed the tasks, we found important insights about what helps students build accurate models.Ìý

Our results show that working together smoothly and sharing ideas well is very important for overall success, especially during the stages when students are doing the work, checking their progress, and thinking about their results. On the other hand, differences in fairness and how often each student spoke had only a small effect on how well students performed during each part of the task.Ìý

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Fig. 1.ÌýCSTEM environment.

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