This research-to-practice full paper examines the integration of culturally responsive computing (CRC) within high school data science education. It presents a theoretical and practical framework that leverages cultural backgrounds to enhance the teaching and learning processes in computational data mining. The main objective of this research is to demonstrate how integrating CRC can improve educational experiences by making them more inclusive and engaging for students from diverse cultural backgrounds. By applying CRC, educators can tailor learning experiences that transmit technical skills and conceptual knowledge and resonate more deeply with students' personal and social identities. This approach has the potential to foster deeper engagement and understanding of the material, thereby enhancing learning outcomes. Our research operates within the broader context of ongoing efforts to integrate data science across K-12 education, advocating for educational practices that acknowledge and utilize students' cultural identities as assets rather than deficits. The theoretical foundations for these practices are rooted in existing literature that emphasizes the importance of culturally relevant pedagogies in computing and data science education. Implementing these frameworks in classroom settings represents a vital intersection of educational research and practice, aiming to address disparities in engagement and achievement among minority students. This work leverages qualitative analysis of how high school students utilize their cultural and social orientations within a structured “coding like a data miner” workshop. The dataset includes students' final project presentations, which showcase their ability to integrate cultural relevance into their epistemic data science practices. We highlight how these integrations influence learners' understanding of and engagement with data science concepts. The results are anticipated to contribute to the discourse on how data science education can be made more accessible and effective through culturally responsive methodologies. This contribution is valuable for preparing a diverse student population to thrive in a data-driven future, ensuring they are equipped with technical skills and can apply them within their cultural contexts.

This research-to-practice full paper describes a transformative approach to computer science education that leverages Open Government Data to bridge the gap between theoretical knowledge and practical application, empowering K-12 and higher education students to engage in open inquiry and contribute meaningfully to societal challenges. In the con-temporary landscape of computer science (CS) education at K-12 and higher levels, a visible shift towards data science-based teaching has emerged as a pivotal focus across diverse disciplines and age groups. The predominant goal is to equip students with the skills and mindset needed to actively participate as informed citizens, engaging in inquiries grounded in data that intersect with social and civic phenomena. This paradigm shift is catalyzed by the exponential growth of data production and a societal trend towards openness and information sharing, both of which are transformative forces shaping the economy and society. Numerous initiatives have been undertaken to enhance CS skills among K-12 students, with notable programs such as Bootstrap Data Science (BDS), Coding Like a Data Miner (CLDM), and Exploring Computer Science (ECS) leading the charge in integrating data science into the educational framework or curriculum using different perspectives on coding, social media data, and curated dataset. Despite these commendable efforts, a pressing concern appears over the efficacy of current pre-college data science-based CS education strategies. Often, these strategies involve the utilization of datasets and investigations curated by external entities, limiting learners' authentic practice, and constraining their exploration of meaningful lines of inquiry. This replication-centric approach impedes the development of comprehensive knowledge and mastery, hindering the cultivation of literacies with agency that allows learners to pursue per-sonal interests or address pertinent social issues. Simultaneously, governmental bodies at the local (e.g. opendataphilly.org), state (e.g. data.pa.gov), and federal levels (e.g. data.gov) have actively engaged in this educational evolution by opening up their data for access and reuse by public and private entities. The global phenomenon of Open Government Data has gained momentum in recent years, driven by the belief that its utilization possesses the potential to generate both economic and social value. However, there exists a substantial gap in effectively engaging students to harness this valuable resource for the creation of social value and it is due to a limited number of tools and curricula that create the intersection in the learning process. This paper investigates the critical intersection of data science and computer science education, proposing a transformative approach that em-powers learners through open inquiry using Open Government Data from Local (e.g. opendataphilly.org) State(data.pa.gov), and Federal(data.gov). By advocating for hands-on experiences that involve students in the generation and analysis of data relevant to real-world and local, state, and federal issues, our proposed framework seeks to bridge the gap between theoretical knowledge and practical application. The framework includes the use of a data science tool that explores open government data, connects students' interests in an area (e.g. economy, public health, transportation), and helps in mining, analyzing, and visualizing based on student socio-cultural perspective through curriculum. Through a comprehensive exploration of Open Government Data, we aim to pave the way for a new era in Data Science-based CS education that not only equips students with technical skills but also inspires them with a sense of social responsibility and the ability to contribute meaningfully to societal challenges.

This (research full paper) meta-synthesis explores why researchers study the challenges faced by international graduate students in STEM education at U.S. universities. Despite their crucial role in diversifying the academic and technological landscape with their unique experiences and perspectives, these students face challenges adapting to unfamiliar academic and cultural settings. Unfortunately, existing literature often overlooks the specific experiences of engineering students, particularly those from non-English speaking backgrounds. Research often generalizes international student experiences or focuses only on English-speaking contexts. Without enough support, the talent pipeline is in danger, which is worrying because international graduate students are vital for maintaining diversity and fueling economic growth and innovation in the United States. Understanding researchers' motivations in exploring this field is important to grasp its significance and identify areas necessitating further investigation. Through a systematic review of existing literature, this study aims to uncover the reasons behind research on academic adjustment, support mechanisms, and success rates among international graduate students in the United States This work-in-progress scholarship of research is guided by the question of what motivates current research on academic adjustments, support, and success among international graduate students in Science and Engineering programs within the United States. We identified 38 articles from a pool of 14,765 articles from online databases using our inclusion-exclusion criterion. Each article then underwent a thorough evaluation based on our selection criteria and quality evaluation rubric to ensure the rigor and validity of the final selection process. We analyzed the data and categorized the motivations by themes by using an inductive approach, identifying themes based on what we observed in each paper. Theoretical frameworks such as social cognitive theory and acculturation theory are then applied to discuss our findings and analysis. We used this theory because it shed light on where we might develop this research. Our findings suggest that understanding the intersection of acculturation theory and social cognitive theory can illuminate the institutional support and psychological factors influencing international graduate student's academic success in science and engineering fields. We identify three key motivation areas: Institutional, Social/Cultural, and Psychological. We observe a variety of motivations driving work in this field, such as addressing issues like racism and discrimination, understanding how cultural experiences affect success, and exploring new ideas like resilience to support students' adjustment. These reasons push us to tackle problems such as racism, learn how culture affects success, and help students stay strong. For a more comprehensive understanding, we suggest combining ideas from different theories to understand what helps international graduate students succeed. We think it's important to study more kinds of students and make research methods better. This can help us make a fair environment for everyone in universities.

students to learn in order to succeed in an increasingly data-driven world. Foundational data literacy skills currently live in a number of subjects across K-12 (e.g., data collection and analysis in science classes, statistical calculations in mathematics/statistics, data visualization and communication in civics/social studies), however, a growing number of schools and districts are introducing stand-alone data science (DS) courses. Given the centrality of computing and programming in the contemporary practice of DS, many of these courses include topics historically reserved for computer science (CS) classes. Further, many CS courses include dedicated time for DS topics (e.g., AP Computer Science Principles' unit on Data). In many ways, DS educators and CS educators are working towards the same ends in complementary ways. However, at other times, the two disciplines are in tension, especially given the scarcity of time in K-12 student schedules for non-core subjects. This panel will explore what DS education and CS education can learn from each other, how each can contribute and advance the goals of the other, and how these two intertwined disciplines can productively live alongside each other in K-12 settings.

Personal health tracking devices and internet-based digital platforms with the capacity to collect, aggregate, and store data at massive scales are examples of tools that have broadened priorities in computing to include data science. In response, there has been growing attention in research and practice emphasizing pre-college groups. This is partly because of the growing recognition-reflected in initiatives like CS4ALL, Code.org, Bootstrap: Data Science, Exploring Computer Science-that learning experiences before college are consequential in sustaining a robust pipeline of computer scientists and engineers. Despite these inroads, there is justifiable concern that existing efforts might not fully support learner development in the necessary conceptual, epistemological, and heuristic styles needed to productively parse and understand “big data.” This is because computing-based curricula that include data science often involve data curated by others (rather than learners directly), which results in simulated versions of practice instead of engagement that is realistically discursive and messy. This is further complicated by the persistent shortage of K-12 computer science teachers in general and even fewer who can design and implement curricula that support authentic engagement with data science. To address these issues, we leverage culturally relevant and constructionist perspectives in a sandbox (i.e., open-ended) science where tools like Scratch and electronic textiles (E-textiles) have had success expanding possibilities in computing to also include activities where learners can engage broadly along varied pursuits-and encounter challenges that spur computational thinking and problem-solving. The literature suggests that learning activities framed in this way encourage knowledge construction, practice literacies, and seriously impact learner attitudes, interest, and perceptions of growth in the field. This latter set of self-concept measures represents a few of many related key predictors of long-term field participation and persistence. In this work-in-progress scholarship of discovery research, we co-develop, with youth and educators, “Coding Like a Data Miner” (CLDM)-a sandbox approach to computing-based data science wherein learners access a social media platform, Twitter, to mine, analyze, and understand quantitative and qualitative data sources. In this preliminary work, we assess affordances in co-developing a curriculum that leverages sandbox approaches to data science. Ultimately (and what will be presented in our final submission), we aim to study learning outcomes when high school students' access, analyze and make sense of “big data” sets of their own. We collaborated with high school teachers in a West Texas/Paso Del Norte region where computer science educators are exceptionally scarce and where there is an urgent and persistent need to support underrepresented learner access to burgeoning areas of computing. Using mixed-methodological approaches (e.g., quantitative analysis of learner pre- and post-survey responses along with qualitative assessments of semi-structured interview data), we address the following research questions: (1) What affordances exist using co-design approaches to develop sandbox data science for pre-college learners? (2) Which computational concepts do students learn when carrying out CLDM activities, (3) Which computational practices do high school students enact when mining, processing, and analyzing big data sets in CLDM? (4) How do learner knowledge and perceptions about data science shift after participating in CLDM? We use contemporary perspectives in computing education, constructionism, and equity to discuss how open-ended sandbox approaches to computing-based data science support learner computational thinking, practice literacies, and field perceptions.

Many efforts of curricula design have concentrated on expanding participation in K-12 CS education by introducing innovative approaches but few have focused on addressing longstanding equity issues through their choices of culturally relevant materials and activities. In this paper, we describe our efforts in using electronic textiles which include Arduino-based microcontrollers that are sewn with conductive thread on fabrics to connect actuators and sensors and create interactive wearables. We report on the implementation of an electronic textiles curricular unit in the Exploring Computer Science introductory computing course in 13 high schools involving 272 high school students largely from underrepresented groups in a major metropolitan school district. We examined two issues relevant to broadening equitable participation in CS: (1) students' changed perceptions of computing, and (2) students' depth of learning of computing, circuitry and crafting in the final project. Pre/post surveys on students' perceptions of computing showed positive, significant gains in students' self-confidence in solving CS problems, fascination with computing and ability to be creative with computing. Teacher evaluations of students' final projects revealed robust learning in the areas of basic programming and computational circuitry as well as strong learning across more challenging computational concepts, with room for growth. We discuss factors that impacted student outcomes and outline steps for further analysis.

We report on the development and implementation of biomakerlab, a wetlab starter kit for synthetic biology activities in K-12. In synthetic biology, participants make their own DNA-gene by gene-and then grow their designs into real applications by inserting them into microorganisms to develop different traits and characteristics provided by the genes. High school students worked with biomakerlab to make logo designs using microorganisms they manipulated to produce differently colored pigments. Our analysis focuses on student engagement with production activities and design challenges in biomaking. In the discussion, we address differences and overlaps between traditional maker activities and biomaker activities for education.

Most of the previous research has focused on gendered or racial representations in either game playing or making contexts. In this paper, we adopt an intersectional perspective by examining gender and race identity representations and constructions within Minecraft, a sandbox game, popular with millions of youth. Our research focuses not only on game play and construction but also examines gender and racial representations within the larger metagaming community, in particular online YouTube video postings and a public gaming convention. In the discussion, we address gender, race, and intersectional identity representation across Minecraft's ecology in how these representations on and offline are influenced by and impact youth gamers along with ways to prevent Minecraft from becoming an exclusionary space.

In this paper, we examine students? learning about computing by designing, coding, and remixing electronic textiles with sensor inputs and light outputs. We conducted a workshop with 23 high school students ages 16-17 years who learned how to craft and code circuits with the LilyPad Arduino, an electronic textile construction kit. Our analyses not only confirm significant increases in students' understanding of functional circuits but also showcase students' ability in reading, remixing and writing program code for controlling circuits. In our discussion, we address opportunities and challenges of introducing codeable circuit design for integrating maker activities that include engineering and computing into K-12 classrooms.