As data science gains prominence in K-12 education and computing standards, there is a growing need for curricula that introduce students to data science from a computational perspective. The API Can Code curriculum addresses this by introducing high school students to the computational foundations of data science through Computer Science-forward, interest-driven, data exploration activities. The curriculum consists of three units and a culminating project in which students write programs in EduBlocks to retrieve data from public APIs and analyze and visualize it using CODAP. This experience report details the design and implementation of the curriculum, with a particular focus on the computational foundations of data science. We share a student project example and analyze post-survey results and teacher reflections to understand how the curriculum shaped students' engagement and perceptions of data science. We discuss the associated challenges and implications of integrating data science practices into computer science settings, and how such experiences can broaden participation in computing. This work advances our understanding of the relationship between data science and computer science in high school classrooms. It offers a novel approach that lays the computational foundations of data science by situating it within students' lived experiences.

As data science (DS) grows in importance across disciplines, its integration into K-12 education remains fragmented, especially when the high school curricula are already packed. Embedding DS within existing computer science (CS) courses offers a practical solution by leveraging overlapping skills and content. To make the case for DS in CS classrooms, an important step is to examine how DS fits into the landscape of CS education through educational standards. This study explores the extent to which core high school DS topics align with the Computer Science Teachers Association (CSTA) standards, a widely adopted framework guiding K-12 CS education in the U.S. We conducted a qualitative document analysis comparing the high school CSTA standards to 61 DS subtopics synthesized from leading DS curricula and frameworks. Two researchers independently coded each pair of CSTA standard and DS subtopic for explicit or implicit connections, resolving discrepancies through consensus. To visualize alignment patterns, we developed an interactive Sankey diagram that maps the connection between the two frameworks. Our findings show that 16 of 17 DS topics align with at least one CSTA standard, primarily within the "Data & Analysis" and "Algorithms & Programming" concept areas. However, alignment often relies on broad or open-ended standards that flatten distinctions between DS practices at various level. Meanwhile, critical DS areas such as machine learning remain unaddressed. This analysis reveals both the promise and limitations of embedding DS in CS classrooms, informing future efforts to integrate DS meaningfully in K-12 education through standards-based curriculum design.

Integrating data science into K–12 education requires accommodating its interdisciplinary nature in the traditionally subject-based curriculum structure. This makes it essential to examine how existing educational standards adequately support or fall short of supporting the learning of data science. This study investigates how data science concepts align with five nationwide educational standards for grades 9–12 in the United States: the Computer Science Teachers Association standards, the Next Generation Science Standards, Common Core State Standards for Mathematics and for English Language Arts and Literacy, and the College, Career, and Civic Life Framework for Social Studies. We conduct qualitative content analysis to identify the manifest and latent connections between the five standards and core data science topics. The findings reveal opportunities and challenges for the interdisciplinary integration of data science content both within and across subjects, providing a foundation for developing future integration roadmaps.

Data is everywhere. Preparing students to navigate and succeed in a data-driven world is an increasingly important role for secondary education. Given its ubiquity, there is tremendous potential to situate data science in students’ lived experiences. Toward this goal, this paper investigates ways to make data science learning experiences authentic. We explore how high school students perceive the authenticity of the datasets, tools, and learning activities they engage with in an interest-driven data science curriculum. Drawing from student reflections, survey responses, and in-depth analysis of final projects, we examine how the curriculum aligns with the four dimensions of authenticity proposed by Shaffer Resnick (1999). The analysis reveals that students recognized the value of working with real datasets and tools, which enhanced their understanding of data science as a meaningful and applicable discipline. Students reported feeling like data scientists when completing learning activities that mirrored real-world tasks. This was particularly true for their final projects, which involved identifying and analyzing data to answer self-generated questions. The paper also presents design characteristics that foster authentic learning and discusses the practical implications for designing such curricula. The insights from this study contribute to the growing body of literature on authentic learning and underscore the importance of designing relevant and authentic curricula that prepare students for the data-rich world that awaits them beyond the classroom.

Switch mode blocks allow learners to write text-based commands inside of blocks within a conventional block-based programming environment. Switch mode blocks can be added by either directly drag-and-dropping them into a program or by right clicking a block and converting it into a Switch mode block. This scaffolded approach can support learners in transitioning from block-based programming to text-based programming. This paper presents an analysis of the eight distinct strategies that learners developed to compose programs using Switch mode blocks. This work contributes to our understanding of how we can design environments that support students of varying levels of prior experience and confidence in transitioning from introductory (block-based) to more powerful (text-based) programming modalities. Ccs concepts • Human-centered computing → Human computer interaction (HCI); Interactive systems and tools.

Live coding for teaching-synchronously writing software in front of students-can be an effective method for engaging students and instilling practical programming skills. However, not all settings are conducive to live coding and not all instructors are successful in this challenging task. We present results from a study involving university instructors, teaching assistants, and students identifying both barriers and benefits of live coding. Physical infrastructure, a positive classroom community with psychological safety, and opportunities for teacher development are practical considerations for live coding. In order for live coding to be an active learning experience, we recommend that tools support multiple mechanisms for engaging students, directing audience attention, and encouraging student-led live coding.

Scratch Encore (Canon Lab, n.d.) is a culturally relevant, student-centered, 14 module, computer science curriculum for 4th to 8th-grade learners that introduces foundational computing topics using the Scratch environment. It employs three key design goals: (a) supporting teachers, (b) supporting learners, and (c) using culturally responsive practices to address long standing inequities in computing. The curriculum offers equitable and effective learning experiences for students who have historically not had equal opportunities to fully participate in computing while providing a wide array of supports for educators who may be inexperienced with Scratch and/or programming. This article features a high-level overview of Scratch Encore and the first 6 modules in greater detail to help teachers understand the content and pacing of the curriculum.

The emergence of Generative Artificial Intelligence (GenAI) tools broadly, and Large Language Models (LLMs) specifically, are equipping introductory programming instructors with a whole new class of pedagogical tools. While GenAI certainly poses threats to time-honored instructional techniques, it also provides opportunities for new forms of instructional support. In this work, we introduce our strategy for configuring an LLM to serve as a ''rubber duck debugging'' coding assistant to help novice programmers when they encounter difficulties in programming assignments. The key contribution of this work is not in the idea of using LLMs for debugging itself (which has already been demonstrated elsewhere, e.g., [3]) but to demonstrate the ease, flexibility, and pedagogical potential of the strategy. In particular, through carefully crafted prompts and easily accessible platforms, rubber duck LLMs can assist learners with specific questions while also situating those questions alongside larger computer science concepts and computational thinking practices. This work contributes an easily replicated and model-agnostic instructional strategy that productively and responsibly leverages the power of LLMs to assist novice programmers in developing foundational programming skills.

Objectives: Interest plays a central role in learning by shaping what, how, when, where, and why learning occurs. In data science education, where complex concepts, lived experiences, and practical skills intersect, capturing and cultivating student interest can be especially generative. This work explores approaches for designing and evaluating interest-driven data science instructional materials. Methods: This article presents a participatory design study that informs the development of a data science curriculum for high school students. To assess how well learner interests and values are reflected in the resulting curriculum, we used the Integrated Interest Development for Computing Education framework [56], which provides a concrete operationalization of interest that captures its multifaceted nature. Findings: The article demonstrates and discusses how participatory design can be used to identify students' interests and how those interests can be used to inform the creation of an interest-driven curriculum. Further, it highlights how different types of participatory design activities yield insight into different facets of students' interests and identities, which can then be used to design learning experiences. This work shows how the resulting Participatory Design (PD) reflects and harnesses the multifaceted nature of student interest and how it can be leveraged to design learning experiences that connect with learners' lived digital experiences. Conclusions: Participatory design is an effective student-centered approach for tailoring computational learning experiences aligned to students' voices, values, and interests. The use of various participatory design activities revealed different facets of students' interests that informed the creation of an interest-driven curriculum that could not have been created without the input of the students themselves.

The past decade has seen a growing number of culturally relevant K-8 computer science curricula. However, as teachers are the experts on their own classrooms, empowering them to customize instructional materials that draw on the cultural identities and personal experiences of their students can be a powerful strategy. Unfortunately, this process can be challenging and time-consuming. Through a series of co-design sessions with 5th-8th grade teachers, we identified challenges teachers face in customizing instructional materials. Our qualitative analysis of these design sessions reveals three primary challenges: (1) completing the customization process in a timely way, (2) preserving the learning objectives of the original curriculum, and (3) meeting personalized culturally responsive teaching goals. In response, we collaboratively and iteratively designed practical and cognitive scaffolds to support teachers in brainstorming and integrating culturally responsive themes specific to their students and classrooms into an existing structured computer science curriculum. This paper presents both the challenges and the scaffolds, contributing a model for supporting teachers in creating customized computing instructional materials for their particular classrooms.

Puzzles are a core component of many videogames. While research has explored the potential of using puzzles in games to provide players with challenges they enjoy, little is known about how children seek information while solving puzzles in videogames. Using a constructivist grounded theory method, this study examines children's (ages 11–14) puzzle-solving approaches within a game titled GEM of the Forest [name anonymized]. The results show that children relied on two sources of information to solve puzzles: (1) information from the out-of-game world including players' prior game experiences, and (2) information within the game world including instructions, hints, inventory, and feedback. We present an empirically grounded theoretical model to understand children's information seeking behaviors while solving in-game puzzles. This paper contributes a theoretical understanding of children's information seeking behavior and strategies to solve puzzles in games. Additionally, we provide a description of the utility of this framework through design implications for the design of information in technologies that seek to engage children in puzzles.

The demand for computing talent is at an all-time high, but not everyone feels equally welcome or has equal access to pursue opportunities in technology fields. Women, non-binary, Black, Hispanic/Latino/a/x, and first-generation students are the least likely to have access to paid undergraduate internships that lead to job opportunities in technology companies. This experience report discusses two programs designed to address the lack of internships among historically excluded populations: a micro-internship program and a career program. We present data showing the role that each program played in creating professional pathways for racially diverse undergraduate women and non-binary students. Both programs prepare these undergraduates for professional environments by exposing them to tech careers and developing their social capital through professional networking. Quantitative and qualitative data were collected as well as summer employment plans to evaluate the effectiveness of the program. We present the short- and long-term impact of each program on students' career interests and sense of belonging in computing. A summary of lessons learned from implementing each program are also shared for stakeholders interested in designing similar programs.

The rise of new technologies, platforms, and computational tools is having a significant and meaningful impact on the ways youth experience and interact with the world. To prepare youth to thrive in these new computational and technological spaces, new skills, concepts, and practices need to be integrated into learners' K-12 experiences. This special issued asked the IJCCI community: What are the critical emerging computational and digital literacies that youth are, or will be, interacting with in the coming years? And, What is the role of K-12 education in preparing youth to engage with and excel in a world where these literacies are needed? The answers to these questions provided by the community constitute this special issue. In doing so, it provides a snapshot of what we currently view as the critical emerging computational and digital literacies and the roles they can and should play in K-12 education.

We live in an increasingly computational world where youth are impacted by computing technologies and algorithms whether they are using them directly or not. The omnipresence of technology and the impacts it has on society lead to Threats to Techquity, or the aspects of computing and technologies that cause or could cause inequity, especially inequity based on marginalized identities. We partnered with youth and teachers to co-design the Talking Techquity curriculum, a middle school curriculum introducing Threats to Techquity alongside computer science skills. After co-designing the curriculum, the teacher partners taught it to sixth grade students. This paper introduces the Talking Techquity curriculum and explores the successes and challenges of teaching it using data from teacher interviews. This work expands efforts to teach students about the social justice and equity impacts of computing and offers an example of how Threats to Techquity can be brought into the classroom.

This paper presents a pilot study of an interest-driven data science curriculum for high school students. The curriculum uses authentic and meaningful data exploration activities to situate data science in students' lived experiences. The curriculum aims to lay the computational foundation of data science and equip students with the necessary skills and practices to become informed and active citizens in our data-driven world. The pilot study, conducted in two sections of a computer science class, demonstrates the curriculum's inquiry-based approach, which allows students to formulate questions based on their interests and answer them by manipulating publicly available datasets. The study illustrates how a block-based learning environment and API data retrieval can be harnessed to support data science learning activities that situate the topics in learners’ lived experiences and create an engaging learning experience. The study advances our understanding of ways to use novel technologies to introduce learners to data science, emphasizing the practical implications of using authentic data and the inquiry-based approach in curriculum design.

Data science is fast becoming a topic of interest not just for university students, but at the high school level as well, as we have highlighted in the Minding the Future column over the years.In this article by Weintrop and Israel-Fishelson, the authors describe one way to identify data sets of relevance to the lives of high school students and to integrate them into a course curriculum.Such integration can help to enhance students' excitement about data science, as they see how the field allows them to answer questions about subjects near to their hearts, be it music, sports, film-or something else altogether.Teachers: Have you explored new ways of getting your students excited about data science?If so, consider sharing your experiences with others!

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.

IntroductionThe foundational practices of Computational Thinking (CT) present an interesting overlap with neurodiversity, specifically with differences in executive function (EF). An analysis of CT teaching and learning materials designed for differentiation and support of EF show promise to reveal problem-solving strengths of neurodivergent learners.MethodsTo examine this potential, studies were conducted using a computer-supported, inclusive, and highly interactive learning program named INFACT that was designed with the hypothesis that all students, including neurodivergent learners, will excel in problem solving when it is structured through a variety of CT activities (including games, puzzles, robotics, coding, and physical activities) and supported with EF scaffolds. The INFACT materials were used in 12 treatment classrooms in grades 3–5 for at least 10 h of implementation. Pre-post assessments of CT were administered to treatment classes as well as 12 comparison classes that used 10 h of other CT teaching and learning materials. EF screeners were also used with all classes to disaggregate student results by quartile of EF.FindingsStudents using INFACT materials showed a significant improvement in CT learning as compared to comparison classes. Students with EF scores in the lower third of the sample showed the greatest improvement.DiscussionThis study shows promising evidence that differentiated activities with EF scaffolds situated across several contexts (e.g., games, puzzles, physical activities, robotics, coding) promote effective CT learning in grades 3–5.