National frameworks for science, technology, engineering, math- ematics, and computer science (STEM+CS) education aim to inte- grate CS within K-12 science classrooms. However, the ways that elementary teachers verbally support CS integration during classroom enactment of STEM+CS projects has rarely been considered in research. This paper uses a descriptive, single case study methodology in the bounded context of a public elementary school to explore how two fifth-grade science teachers implicitly and explicitly support the integration of STEM+CS disciplines within a CS-focused lesson through verbal supports that were either planned in the STEM+CS project curricular materials or added by the teachers. Data sources included transcripts of all whole-class discussions that occurred while the two fifth-grade science teachers co-taught a CS lesson that spanned three fifty-minute class periods to two different classes. Two researchers team coded instances of teachers' verbal support of STEM+CS integration, wrote memos for each instance, and graphed the instances on a four-quadrant, analytic framework which was divided along an axis of explicit or implicit verbal support and an axis of planned or added verbal support. The researchers then grouped their memos by quadrant and used pattern coding to look across instances for emerging themes. Findings include that most instances of verbal support for STEM+CS interdisciplinary integration were added by the teachers and implicitly articulated how disciplinary practices related to each other. Implications include recommendations for ways to help teachers to explicitly support the integration of STEM+CS through CS-focused activities within elementary science classrooms.

This innovative practice paper outlines a design-based research study to enhance data science literacy in students using an unplugged approach. With the rapid growth of data and its impact on daily life, there's a need for new educational methods to help students understand and use data effectively. Data science involves math, statistics, computation, AI, and machine learning, but teaching these concepts can be challenging without access to computers or the internet, potentially widening educational inequities. Inspired by unplugged computer science education, this study supports data science education in unplugged classrooms. Initial research focused on creating and testing two learning units on data visualization and summarization using innovative activities and reflection prompts. Analyzing students' work from a pilot with ten eleventh-graders in a low-income public school in a Global South country, the study aims to understand how these activities support data visualization skills and identify limitations. Future work will develop and implement lesson plans to promote data and AI literacy, aiming to make data science education more inclusive across diverse backgrounds.

This innovative practice work-in-progress paper explores the outcomes of a Machine Learning module taught to undergraduate students from different majors with various levels of programming backgrounds. Our approach differs from most machine learning for undergraduate courses, which usually teach about machine learning on a language-centric code-first basis. However, while not everyone needs to deploy a model in production, we should open the black box so they understand what they consume and help build through their data and feedback. Our results will contribute to the growing field of machine learning education as we explore students conceptual understanding about Machine Learning and its algorithms. Machine learning is relatively ubiquitous in many different fields and contexts. There are new developments that include machine learning algorithms that students interact with every day. From the auto-complete feature in their cellphones to the content they are shown while browsing the internet, machine learning algorithms shape the way students experience the world. Therefore, students must critically understand this technology and its implications for their lives. This study focuses on a four-week course module that included learning activities designed for students to develop their intuition regarding the benefits of implementing machine learning before moving to a programming language. Students recognize commercial uses of machine learning algorithms, apply the concepts in a visual programming environment, and reflect on the benefits and limitations of the algorithms. This work-in-progress paper presents the work of 13 participants who completed at least one of the following learning activities. The participants completed reflection activities that represented retrieval practice opportunities to consolidate their learning and a final project to demonstrate their understanding of machine learning and how it may be implemented. We analyzed the qualitative data of student work to describe how the suggested progression supports learning about machine learning.

Elementary teachers within a culturally relevant computer science researcher-practitioner partnership (RPP) co-designed and facilitated short professional development opportunities to share back to the RPP members, internally labeled as micro-PD. We describe the nature of these micro-PDs and share artifacts that the teachers co-constructed and taught as a preliminary model of collaboratively building learning opportunities within an RPP.

This poster reports on the second iteration of a workshop to help elementary teachers create lesson plans that integrate culturally relevant computer science (CS) learning opportunities. Findings include revisions to the workshop from the previous year and reveal the extent to which teachers used supports for culturally relevant pedagogy to refine their lesson ideas. The presentation will discuss results and share workshop artifacts.

This poster reports on a workshop to help elementary school teachers integrate culturally relevant computer science (CS) learning opportunities into their classrooms. Findings highlight the importance of facilitators to help teachers discuss cultural competence and the value of co-developed rubrics to evaluate lessons. The presentation will discuss results and share artifacts from the workshop.

This poster presents pilot results of a professional development (PD) workshop designed by a Research-Practice Partnership (RPP) that focused on helping K-8 teachers integrate computer science (CS) concepts into other content areas using culturally responsive teaching (CRT) strategies. Preliminary results demonstrate that teachers significantly improved their self-efficacy to teach CS and self-confidence to use CRT in CS after the 4-week workshop. Results suggest that K-8 teachers can greatly benefit from PD experiences that focus on equitable CS instruction and pedagogy.