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.

Computer science (CS) education at the university level is often challenging, particularly for students with no prior programming experience. To help scaffold students' CS learning, instructors often utilize systems for automated assessment of programming assignments, where students can individually learn online using automatically generated feedback. However, despite the growing usage of these systems, learning outcomes are often mixed and not all students benefit equally from using these applications. In this study, we utilize Ordered Network Analysis (ONA) to examine data from a system for automated assessment of programming assignments and compare platform activity between novice students (N=110) achieving high (N=43) and low (N=67) scores on the final test of an introductory CS course. We identify and visualize differences in the activity patterns between the groups. High performing novice students tend to request feedback more often, while low performing students more often leave the assignment unsolved after experiencing an unsuccessful attempt. These findings show that Ordered Network Analysis can serve as a useful tool for understanding student behaviors, facilitating the design of targeted interventions that might support learners at key moments in their programming engagement towards task success.

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.

This paper reports the iterative design-based research and implementation of Virtual Ci1ity Planning, a course that leveraged a virtual learning environment (VLE) and supportive classroom curricula to encourage students' exploration of environmental science and urban planning identities. Iterative course design and assessment was informed by Projective Reflection - a theoretical and methodological framework that conceptualizes learning as a role-specific process of identity exploration over time. This work describes the cyclical process of contextual analysis, design and implementation, and efficacy evaluation across three sessions of Virtual City Planning, which were implemented in a science museum with 57 high school students. The design case demonstrates how each session was modified to adapt to contextual needs and encourage deeper and more integrated processes of identity exploration as defined by Projective Reflection. The work concludes with lessons learned for future research on identity exploration in VLEs.