Introducing AI concepts in the earliest years of schooling can help children make sense of intelligent technologies, yet few resources exist for K–2 classrooms. This paper presents the design and outcomes of a professional development (PD) program supporting K–2 teachers as they explored AI literacy and co-designed unplugged classroom activities. Grounded in AI4K12's Five Big Ideas in AI framework, the PD combined hands-on learning, collaborative design, and micro-teaching opportunities. Guiding activities included Train the AI (pattern recognition), What Happens Next? (consequences of AI use), Who Did the Robot Hear? (data diversity), and Teach the Robot (model training). Educators then created screen-free, English Language Arts-aligned activities using storytelling, sorting, and embodied play to introduce AI topics such as machine learning and fairness in age-appropriate ways. The PD emphasized integrating AI into existing K–2 literacy routines, lowering implementation barriers while supporting vocabulary development, reasoning, and empathy. Teacher reflections revealed growing confidence in adapting AI topics for young learners and highlighted the value of peer collaboration, clear language, and tactile materials.

As large language models (LLMs) and chatbots become increasingly prevalent, there is an urgent need to create engaging, age-appropriate learning activities that foster foundational AI literacy with a focus on natural language processing (NLP). This paper presents the iterative design and implementation of three instructional activities that introduce middle school learners (ages 11--14) to NLP concepts through playful, hands-on experiences aligned with the AI4K12 Big Idea of Natural Interaction. These activities include: (1) an unplugged card game that develops students' understanding of embeddings and similarity, (2) an unplugged collaborative sentence-generation challenge that illustrates how language models work, and (3) a web-based educational game in which students design and interact with chatbots. Each activity was implemented and refined across multiple educational contexts, including teacher professional development workshops, summer camps, and classroom implementations. All activities are designed to be easy to set up, requiring only commonly available classroom technology (e.g., laptops) and a few inexpensive materials (e.g., decks of cards), and are supported with facilitation guides and reflection prompts. Early implementations revealed areas for refinement, leading to clearer scaffolding that helped students connect gameplay to underlying NLP concepts, and post-refinement surveys indicated that students found the activities both enjoyable and educational. Findings suggest that blending unplugged and digital formats enhances comprehension, and that tailoring content to students' local contexts supports engagement. By making these activities openly available, this work contributes to the growing ecosystem of K–12 AI education resources and offers practical guidance for integrating NLP concepts into classroom instruction.

As artificial intelligence (AI) becomes increasingly central to healthcare and biomedical research, there is a growing need for learning experiences that help students understand AI concepts while envisioning future career pathways. This paper presents a theory-informed narrative-centered model designed to foster AI literacy and support emerging interest in biomedical careers among early adolescents aged 11-14. Grounded in narrative-centered learning and social cognitive theory, the model articulates how narrative structure, role-based engagement, and consequential decision making can support self-efficacy development, conceptual understanding, and interest in AI-enabled biomedical work. Building on this framework, we describe the design of a narrative-centered educational game in which learners assume the role of a medical intern and investigate patient cases using AI diagnostic tools. We then report findings from a pilot study with 25 students who played the game and participated in focus groups, drawing on gameplay trace data, in-game reflections, and qualitative feedback. Findings suggest high engagement, productive use of AI tools, and evidence of increased awareness of biomedical applications of AI, along with indications of perceived understanding of AI concepts. Together, the theoretical model, game design, and pilot findings illustrate how narrative-centered educational games can serve as research platforms while providing insight into how youth reason about AI in career-connected learning contexts.

Emotions play a central role in shaping learning within digital environments. Although their effects may depend on how students’ emotional experiences manifest into concrete behaviors, the links between these dimensions remain underexplored. This study investigates the most common behaviors during episodes of boredom, confusion, frustration, and engaged concentration in an educational game, as well as associations with situational interest, self-efficacy, prior knowledge, and learning gains, using interaction logs and sensor-free affect detectors. Results show that boredom is linked to off-task roaming, both consistently associated with lower motivation and learning. In contrast, behaviors during engaged concentration, frustration, and especially confusion vary widely, shaped by motivational traits and prior knowledge and offering diverse associations with learning. Concrete regulatory responses in these states—such as systematizing findings with in-game tools, skimming domain content to resolve doubts, or testing hypotheses—are positively associated with learning and motivation, reflecting students’ ability to regulate emotions and address cognitive challenges. However, less constructive responses, such as aimless wandering, were tied to lower knowledge and motivation, underscoring the need for additional support. These findings extend existing affective theory by underscoring the importance of considering the behavioral dimension when analyzing students’ emotions in digital learning environments.

Active learning in introductory programming depends on frequent, high-quality feedback, yet instructors often struggle to deliver consistent support at scale. In this poster, we introduce INSIGHT, an AI-driven classroom assistant designed to promote active learning in introductory programming (CS1) courses through scalable, personalized, and explainable feedback. The assistant combines the generative capabilities of large language models (LLMs) with instructor-in-the-loop authoring and an explainable code analysis engine to ensure pedagogically aligned support. Instructors can co-design problems with LLM assistance, provide exemplar solutions, define common student errors, and author targeted feedback. The AI engine analyzes student code submissions, identifies misconceptions, and maps them to instructor-verified feedback in real time. INSIGHT is designed to ensure that key educational concepts and common misconceptions are explicitly addressed by instructors, while also leveraging LLMs to provide reasonable feedback for novel or edge-case solutions that instructors may not have anticipated. By combining instructor expertise with the flexibility of generative AI, the assistant helps close feedback gaps and ensures more comprehensive coverage of student learning needs, especially in large or diverse classrooms with limited instructional support.

Early elementary teachers are eager to introduce artificial intelligence (AI) concepts to their students but lack age-appropriate resources for exploring abstract ideas such as bias, trust, and fairness. This poster reports on a professional development (PD) program that engaged K-2 teachers in co-designing unplugged, literacy-aligned lessons for AI literacy in resource-constrained classrooms. Grounded in the AI4K12 ''Five Big Ideas in AI,'' the PD combined three phases: concept exploration, lesson co-design, and micro-teaching practice. Through the co-design process, teachers created lessons to help translate abstract AI concepts into classroom-friendly activities. Teachers emphasized the importance of simplifying technical language, embedding activities within familiar English Language Arts (ELA) routines, and using embodied and narrative approaches to sustain student engagement. This work contributes a replicable PD model for introducing AI to early elementary teachers, concrete examples of unplugged AI literacy lessons, and early evidence that abstract AI concepts can be meaningfully connected to literacy and play in K-2 education.

Collaboration is central to computer science (CS) learning, yet little is known about how different collaborative programming models support upper elementary students. Although pair programming with a single computer is well established, younger learners often struggle with enacting driver–navigator roles. Networked environments enable two-computer programming models, where each student uses their own device, with or without structured roles. This poster presents early teacher insights on the feasibility of such models in classroom settings. Teachers highlighted the promise of using two-computers with roles for promoting equitable participation and sustained engagement, while noting practical challenges such as role design, switching, and device availability. Findings underscore the importance of aligning collaborative structures with classroom realities and providing scaffolds and routines to support facilitation, offering implications for both research and classroom practice in elementary CS education.

Integrating computational thinking (CT) into early K-12 education is increasingly recognized as essential for preparing students to navigate a technology-driven world. Digital storytelling, with its capacity to combine narrative expression with programming, offers a promising interdisciplinary strategy for promoting CT. This experience report presents a narrative-centered learning environment that integrates digital storytelling, block-based programming, and hands-on maker activities to foster CT and interdisciplinary learning in upper elementary classrooms. Grounded in a problem-based storyline, the environment engages students in solving real-world-inspired challenges through physical science experimentation and interactive narrative creation. We describe a multi-week classroom implementation with fourth- and fifth-grade students and analyze survey responses and programming artifacts from 41 participants to explore how CT concepts and practices, including sequencing, conditionals, and debugging, emerged in their work. While students demonstrated statistically significant gains in CT, they also encountered challenges related to narrative coherence, science alignment, and conditional logic. We reflect on what did and did not work, offering design insights for educators and designers adopting interdisciplinary, story-driven approaches to computing education.

Adoption of artificial intelligence (AI) is at an inflection point. With daily use of AI escalating due to widely available software tools, educators, researchers, and policymakers must adapt swiftly to changing educational needs. While think tanks and Big Tech companies often promote the notion that AI serves as a powerful tool for democratizing access to knowledge and opportunities, our work in rural communities underscores the disparity in access to AI education and related opportunities. In this paper, we report on our experience introducing foundational AI concepts to rural middle school students using an unplugged game-based learning activity. By providing engaging learning experiences to rural populations, we hope to broaden interest in and understanding of AI technologies. To this end, we conducted a classroom study in which two middle school teachers implemented our unplugged AI learning activity with their students. Analyzing survey data from 60 of the participating students, we explore the impact of the activity on their interest in AI, their conceptual understanding, and examine potential gender differences. Additionally, we share insights from the teachers who participated in our professional development sessions in preparation for the classroom implementations.

As AI becomes increasingly central to healthcare and biomedical research, there is a growing need for tools that introduce students to AI in engaging, authentic contexts. This paper presents AI4Health, a narrative-centered educational game that places middle school students in the role of a medical intern investigating real-world health mysteries. In the game's first episode, players gather evidence, interview characters, and use machine learning models with different training data and performance characteristics. Through branching dialogue and contextual problem-solving, players explore the impact of training data on model reliability and consider the ethical implications of AI-assisted diagnosis. This paper showcases the core mechanics, dialogue system, and interactive narrative structure that support AI literacy and health career exploration, and highlights plans to extend the game through additional biomedical scenarios and classroom integration.

As artificial intelligence (AI) continues to transform industries across society, it is having a profound impact on healthcare and biomedical research. To prepare students for this evolving landscape, there is a growing need for learning experiences that build foundational AI literacy and connect to real-world career pathways. However, most middle school students lack access to engaging and personally meaningful opportunities in AI education and career exploration. NarrativeCentered Learning (NCL) offers powerful affordances for contextualizing AI literacy through engaging storylines and role-based problem-solving. In parallel, Social Cognitive Career Theory (SCCT) emphasizes how students' beliefs about their abilities, expectations about outcomes, and personal goals influence the development of their academic and career trajectories. This paper introduces a design framework that integrates NCL and SCCT to foster both AI literacy and health-related career interest. We apply this framework to the design of a narrative game in which students take on the role of a medical intern investigating virtual patient cases using AI tools. We report findings from a usability study with 25 middle school students who played the game's first episode and participated in structured focus groups. Student feedback suggests that the game supported engagement, sparked curiosity, and encouraged emerging career interest. These findings offer preliminary support for the framework and inform the design of career-connected AI learning.

For more than a decade, a handful of theoretical models have shaped a substantial amount of the research related to students’ emotional experiences during learning. This research has been productive, but articulating the underlying implicit assumptions in existing theories and their implications in our empirical interpretations can help to better investigate the reciprocal relationships between learning and emotion, and subsequently, to develop better interventions. This paper expands upon the existing theoretical frameworks, increasing the types of questions we ask about affect dynamics. We do so within the context of Crystal Island, a virtual world that allows middle school students to investigate microbiology questions. Specifically, we use this data to examine and revise the assumptions that are implicit in these models and the methods we use to investigate them.

Engaging middle school students in complex computational topics such as AI can pose unique challenges to educators, ranging from simplifying potentially difficult mathematical material to maintaining student interest in the subject. One approach to help address these challenges is to utilize hands-on, real-world examples. We conducted a week-long summer camp for 24 students, centering each day around activities aligned with one of the Five Big Ideas in AI. Students participated in exit ticket reflections following each activity. The pathfinding activities, which occurred on one of the days, incorporated real-world examples and digital simulations of three pathfinding algorithms (breadth-first search, depth-first search, and A*), including an activity modeled after the game Pac-Man. Thematic analysis of exit-ticket responses revealed four major themes regarding students' key takeaways from the activities: (1) pathfinding for character movement, notably in video games like Minecraft; (2) pathfinding as a means of efficient navigation; (3) theoretical reasoning regarding the speed of the A* algorithm compared to others, highlighting its intelligent search mechanism; and (4) empirical reasoning based on personal experience during activities, where some students noted A* consistently performed fastest. These findings indicate that students not only engaged with AI concepts but also demonstrated a nuanced understanding of algorithmic efficiency. We examine the implications of these findings on understanding student engagement with interactive pathfinding activities and highlight the potential for future work in this area.

Student affect is widely recognized as a major influence on learning gains and engagement, which has led to the development of many automated affect detectors. However, in order to respond effectively to student affect, we must know how students interpret it. This study proposes a novel automated detector that models when students attribute their epistemic emotion to task difficulty. The goal is to use detectors like this one to better understand how to respond to students' affective states (in this case, boredom, confusion, frustration and nervousness). We then discuss the implications of this novel detector for real-time support in game-based learning environments.

With the growing prevalence of AI, the need for K-12 AI education is becoming more crucial, which is prompting active research in developing engaging and age-appropriate AI learning activities. Efforts are underway, such as those by the AI4K12 initiative, to establish guidelines for organizing K- 12 AI education; however, effective instructional resources are needed by educators. In this paper, we describe our work to design, develop, and implement an unplugged activity centered on facial recognition technology for middle school students. Facial recognition is integrated into a wide range of applications throughout daily life, which makes it a familiar and engaging tool for students and an effective medium for conveying AI concepts. Our unplugged activity, “Guess Whose Face,” is designed as a board game that focuses on Representation and Reasoning from AI4K12’s 5 Big Ideas in AI. The game is crafted to enable students to develop AI competencies naturally through physical interaction. In the game, one student uses tracing paper to extract facial features from a familiar face shown on a card, such as a cartoon character or celebrity, and then other students try to guess the identity of the hidden face. We discuss details of the game, its iterative refinement, and initial findings from piloting the activity during a summer camp for rural middle school students.

Recent years have seen growing awareness of the need to advance AI literacy for K-12 students to empower them in understanding, evaluating, and using AI. Automated problem solving is a fundamental aspect of AI, enabling machines to mimic human problemsolving abilities. Fostering awareness and interest in AI capabilities such as automated problem solving should begin early, including in the elementary grades. Although AI planning can be a complex topic, leveraging the benefits of game-based learning offers a promising approach to engage young children in learning about this important AI concept. In this work, we explore the interactions and outcomes of upper elementary students (ages 8 to 11) playing a quest on AI planning embedded within a game-based learning environment. Results indicate that students experienced positive learning gains from pre-test to post-test, while analyzing trace data from the game provides insights into challenges students faced as they attempted the in-game missions.

As the presence of artificial intelligence (AI) technologies increases throughout everyday life, so does the need to engage rural communities in AI learning experiences, as these communities often have limited access to such educational opportunities. This work presents three game-based learning activities rooted in core AI concepts: natural language processing, search, and reinforcement learning. These activities were implemented in a summer camp with middle grades students in a rural area of the USA. We share an overview of the activities, as well as key observations and takeaways from student responses in post-activity surveys.

Identifying misconceptions in student programming solutions is an important step in evaluating their comprehension of fundamental programming concepts. While misconceptions are latent constructs that are hard to evaluate directly from student programs, logical errors can signal their existence in students' understanding. Tracing multiple occurrences of related logical bugs over different problems can provide strong evidence of students' misconceptions. This study presents preliminary results of utilizing an interpretable state-of-the-art Abstract Syntax Tree-based embedding neural network to identify logical mistakes in student code. In this study, we show a proof-of-concept of the errors identified in student programs by classifying correct versus incorrect programs. Our preliminary results show that our framework is able to automatically identify misconceptions without designing and applying a detailed rubric. This approach shows promise for improving the quality of instruction in introductory programming courses by providing educators with a powerful tool that offers personalized feedback while enabling accurate modeling of student misconceptions.

With the growing prevalence of AI, the need for K-12 AI education is becoming more crucial, which is prompting active research in developing engaging AI learning activities. In this paper, we present our work on a game construction toolkit for middle school students and educators that enables them to tailor an AI-focused unplugged card game activity. In our prior work, we designed, developed, and piloted an unplugged card game activity where players predict the identity of a person based on hand-drawn features extracted from a set of facial cards. The activity aims to teach AI concepts aligned with one of the big ideas in AI utilizing techniques from facial recognition. During our pilot testing of the activity, we discovered that creating face cards that capture students' interest is a crucial factor in promoting student engagement. As a result, we designed a card game construction toolkit that allows students and educators to craft their own face card decks using photos that are personally interesting to them, looking to foster engagement and improve replayability of the activity. The toolkit's design is focused on ensuring easy accessibility and features a simple web-based interface that allows users to download and print their customized cards. We expect this toolkit will enhance the usability and educational effectiveness of our unplugged K-12 AI education activity.

Recent years have seen increased exploration of the transformative potential of digital games for K-12 education. Narrative-centered digital games for learning integrate complex problem solving within compelling interactive stories. By leveraging the inherent structure of narrative and the engaging interactions afforded by commercial game engines, narrative-centered digital games for learning engage students in situated learning activities. This article presents details on the iterative design and development of a narrative-centered digital game for learning that focuses on science education for fifth-grade students. We then explore how student gameplay and learning relate by leveraging interaction log data from over 700 students playing the game. Specifically, we analyze student gameplay achievements using clustering and examine how gameplay and learning outcomes differ among the groups identified. Furthermore, we investigate if gender has an effect on student learning within the groups and what gender differences are found within the groups. The findings show that students who complete more quests and earn better in-game rewards achieve higher learning gains, and while differences exist in game playing characteristics between males and females the learning outcomes are similar.

Recent years have seen increased exploration of the transformative potential of digital games for K-12 education. Narrative-centered digital games for learning integrate complex problem solving within compelling interactive stories. By leveraging the inherent structure of narrative, narrative-centered digital games for learning engage students in situated learning activities. In this paper, we investigate how student gameplay and learning relate in a narrative-centered digital game for learning that focuses on science education for fifth grade students. Specifically, leveraging interaction log data from a study with over 700 students we analyze student gameplay achievements using clustering and examine how gameplay and learning outcomes differ among the groups identified. The findings show that students who complete more quests and earn better in-game rewards achieve higher learning gains.

The ever pervasive nature of artificial intelligence (AI) in our world necessitates a focus on fostering an AI literate society. Young children, those aged 11 to 14, are at a critical point in developing their dispositions toward and perceptions of science, technology, engineering, and mathematics (STEM), which influences their future education and career interests. Youth in rural areas are in particular need of access to AI learning opportunities to prepare them for the future workforce; digital games may be one way to attract young, rural students to STEM education and careers. In this paper, we explore how to introduce rural middle grades students to foundational AI concepts through digital game design activities. To inform our efforts and to establish an understanding of what these student populations as well as their teachers know about AI and games, we conducted a set of interviews and focus groups. In brief, students’ awareness and understanding of AI varied significantly, whereas teachers had limited knowledge of AI. Moreover, students shared great interest in playing and designing games. In support of our findings, we are developing a set of game design activities around five core AI concepts and ensuring the activities are of interest to our rural students.

There is a growing need for 21st-century workers to be digitally literate and to possess computational thinking and collaborative problem-solving skills. Computer-supported collaborative learning (CSCL) focused on computational thinking can guide students toward the co-development of these skills. In this work, we present our approach to integrating virtual and physical learning modalities into InfuseCS, a CSCL environment. InfuseCS uses problem-based learning scenarios to situate upper elementary school students (ages 8 to 11) in a CSCL setting to foster their computational thinking and science knowledge construction as they collaborate to create digital narratives.

Children encounter and use artificial intelligence (AI) with regularity, but the depth of their understanding of AI is often limited. In service of growing an AI and technology-literate K-12 population, it is important for young learners to engage in AI learning activities early and often. To foster the design of AI curricula, it is essential to understand what young children already know and how they feel about AI. The nascent field of AI-related self-report instrument development focuses largely on adult populations or AI's use in specific contexts, such as medicine. There remains a critical need to develop an AI attitudinal survey for young learners (ages 9 to 11). Building upon the extant survey development work of those in education and AI, we have designed a brief survey on students' self-efficacy for AI, interest and motivation toward AI, and attitudes toward AI. We used cognitive interviewing processes to ensure the items in the survey were readable and understandable by young students. Preliminary findings indicate young students have mixed understanding of what AI is, what it can do, and how they feel about AI. We discuss implications for researchers and practitioners and provide an overview of our continuing efforts to validate this instrument.

The ubiquity of artificial intelligence (AI) in everyday life suggests the need to ensure young students know about AI, its uses and limitations, and its benefits and risks, while enabling them to develop expertise in using AI-driven technologies. To support rural middle grades students and educators in learning and teaching AI concepts, we are designing AI-focused learning activities centered around the creation of digital gameplay experiences. To inform our designs, we conducted educator interviews and student focus groups to gain insights into their understanding of AI, their computer science background, and their knowledge and interest in gaming. Building on findings from these interviews and focus groups, we have designed a set of hands-on activities to elicit deeper feedback from students and educators on their preferences, points of confusion, and interests. In this work, we present our initial AI-infused game design activities.

Collaborative game-based learning environments have significant potential for creating effective and engaging group learning experiences. These environments offer rich interactions between small groups of students by embedding collaborative problem solving within immersive virtual worlds. Students often share information, ask questions, negotiate, and construct explanations between themselves towards solving a common goal. However, students sometimes disengage from the learning activities, and due to the nature of collaboration, their disengagement can propagate and negatively impact others within the group. From a teacher's perspective, it can be challenging to identify disengaged students within different groups in a classroom as they need to spend a significant amount of time orchestrating the classroom. Prior work has explored automated frameworks for identifying behavioral disengagement. However, most prior work relies on a single modality for identifying disengagement. In this work, we investigate the effects of using multiple modalities to detect disengagement behaviors of students in a collaborative game-based learning environment. For that, we utilized facial video recordings and group chat messages of 26 middle school students while they were interacting with Crystal Island: EcoJourneys, a game-based learning environment for ecosystem science. Our study shows that the predictive accuracy of a unimodal model heavily relies on the modality of the ground truth, whereas multimodal models surpass the unimodal models, trading resources for accuracy. Our findings can benefit future researchers in designing behavioral engagement detection frameworks for assisting teachers in using collaborative game-based learning within their classrooms.

The computing education (CEd) research community's growth is fueled in part by the growing number of CEd Ph.D. graduates, who are also increasingly entering postdoctoral positions and fellowships. While prior work has shown that postdoctoral researchers have positive effects on research labs, provide essential support to graduate students, and serve critical roles in the research ecosystem, there are no existing support structures for postdoctoral researchers within CEd. As postdocs navigate the challenges of their new positions alongside the demands of research and job searches, they would benefit from a community of peers wherein they could share their experiences and learn from others. This Birds-of-a-Feather (BoF) session is an organized space for CEd postdoctoral researchers, including those interested in postdoctoral positions, to build community and share postdoc experiences. Building on prior experience with running a similar BoF space for postdocs in SIGCSE 2022, the BoF discussion leaders is composed of current CEd postdocs, previous CEd postdocs who have taken on academic and research careers, and a CEd researcher from a non-academic space. This combination of diverse experiences, career contexts, and expertise will provide valuable perspectives that will aid in discussions of various postdoc experiences and research and career opportunities. BoF participants will have opportunities to discuss research goals, areas, and activities; exchange advice on navigating career paths and job searches; discuss approaches for mentorship within and outside research labs; and share best practices on the postdoc experience.

Digital storytelling, which combines traditional storytelling with digital tools, has seen growing popularity as a means of creating motivating problem-solving activities in K-12 education. Though an attractive potential solution to integrating language arts skills across topic areas such as computational thinking and science, better understanding of how to structure and support these activities is needed to increase adoption by teachers. Building on prior research on block-based programming for interactive storytelling, we present initial results from a study of 28 narrative programs created by upper elementary students that were collected in both classroom and extracurricular contexts. The narrative programs are evaluated across multiple dimensions to better understand the types of narrative programs being created by the students, characteristics of the students who created the narratives, and what types of support could most benefit the students in their narrative program construction. In addition to analyzing the student-created narrative programs, we also provide recommendations for promising system-generated and instructor-led supports.

The demand is growing for a populace that is literate in Artificial Intelligence (AI); such literacy centers on enabling individuals to evaluate, collaborate with, and effectively use AI. Because the middle school years are a critical time for developing youths' perceptions and dispositions toward STEM, creating engaging AI learning experiences for middle grades students (ages 11 to 14) is paramount. The need for providing enhanced access to AI learning opportunities is especially pronounced in rural areas, which are typically underserved and underresourced. Inspired by prior research that game design holds significant potential for cultivating student interest and knowledge in computer science, we are designing, developing, and iteratively refining an AI-centered development environment that infuses AI learning into game design activities. In this work, we review design principles for game design interventions focused on middle grades computer science education and explore how to introduce AI learning experiences into interactive game design activities. We also discuss results from our initial co-design sessions with middle grades students and teachers in rural communities.

The current study compared American, Korean, and Indonesian middle and high school students’ CS attitudes. Concurrently, this study also examined whether the items in the CS attitudes scale exhibit country and gender measurement biases. We gathered data on CS attitudes from middle and high school students in the US, Korea, and Indonesia. The participating students took the same (translated) previously validated CS attitudes scale. We ran a unidimensional IRT, differential item functioning (DIF), a two-way ANOVA, and the Kruskal-Wallis H test. Despite the valid instrument, we found it inappropriate as is for international comparison studies because students from different countries interpreted some items differently. We then compared gender-based differences in CS attitudes across countries. The results revealed no significant differences between males and females in the Indonesian middle school data, whereas male students had significantly higher CS attitudes than female students in both American and Korean student data. Furthermore, we found the same pattern in gender differences in Korean and Indonesian high school students’ CS attitudes scores as in the middle school study. These findings underscore the importance of a country’s sociocultural context in influencing gap and diversity in secondary school students’ CS attitudes.

The lack of gender diversity in the computer science (CS) field and workforce is a well-documented challenge that many, but not all, countries face. Such a challenge may tie to socio-cultural issues that have impacted K-12 CS education, eventually creating a gender gap in CS performance and attitudes. The current study compared American and Indonesian middle school students' computational thinking (CT) skills and CS attitudes. Concurrently, this study also examined whether the items in the instruments we used exhibit country, gender, or prior CS experience measurement biases. A total of 592 American n = 242 and Indonesian n = 350 middle school students took a CT assessment and CS attitudes scale. Differential item functioning (DIF) was used to detect biased items, and a two-way ANOVA was utilized to examine the interaction effects of country and gender in the two constructs. The results showed some items were flagged as having country-specific DIF. The results also indicated that the American students had higher CT scores than Indonesian students. However, Indonesian students obtained higher CS attitudes scores compared to American students. Further results showed a significant gender difference in CS attitudes in the American samples; however, such a significant difference was not found in the Indonesian sample. These findings underscore the importance of a country's socio-cultural context in influencing gender diversity in the CS field.

Pair programming is a popular strategy in computer science education to teach programming to novices. In this study, we examined the effect of three different pair programming conditions on upper elementary school students' CS conceptual understanding. The three conditions were one-computer with roles (1C with roles), two computers without roles (2C no roles), and two computers with roles (2C with roles). These students were engaged in four days of computer programming activities and took the CS concept assessment, CS attitudes, and collaboration perceptions before and after the activities. We used the validated E-CSCA (Elementary Computer Science Concepts Assessment) to measure elementary students' understanding of CS concepts. We tested the relationship of different pair programming conditions on the students' CS conceptual understanding and found that different conditions impacted students' CS conceptual understanding, wherein students in 2C roles demonstrated better CS learning than the other two conditions. The results also showed no changes in students' CS attitudes and perceptions of collaboration before and after the activities. Furthermore, the results indicated no significant impact of these attitudinal factors on students' learning CS concepts in pair programming settings. Our study highlights the importance of the roles and number of computers in pair programming settings, especially for elementary students.

In successful collaborative paradigms such as pair programming, students engage in productive dialogue and work to resolve conflicts as they arise. However, little is known about how elementary students engage in collaborative dialogue for computer science learning. Early findings indicate that these younger students may struggle to manage conflicts that arise during pair programming. To investigate collaborative dialogue that elementary learners use and the conflicts that they encounter, we analyzed videos of twelve pairs of fifth grade students completing pair programming activities. We developed a novel annotation scheme with a focus on collaborative dialogue and conflicts. We found that student pairs used best-practice dialogue moves such as self-explanation, question generation, uptake, and praise in less than 23% of their dialogue. High-conflict pairs antagonized their partner, whereas this behavior was not observed with low-conflict pairs. We also observed more praise (e.g., "We did it!") and uptake (e.g., "Yeah and...") in low-conflict pairs than high-conflict pairs. All pairs exhibited some conflicts about the task, but high-conflict pairs also engaged in conflicts about control of the computer and their partner's contributions. The results presented here provide insights into the collaborative process of young learners in CS problem solving, and also hold implications for educators as we move toward building learning environments that support students in this context.

Collaborative learning has demonstrated benefits for girls in computer science [7] and this may be a way to help address the gender gap in CS. Research indicates that while collaborating, boys often express more individualistic ideas whereas girls tend to be more supportive [1]. It is important for students to regulate their learning in collaborative learning environments because they need to negotiate group goals and diverse approaches to the task [3], and the use of open-ended tasks with multiple solution paths are common [4]. There is minimal research in CS education on regulation of learning (e.g., [5,6,]). Co-regulated learning is the process in which an other helps regulate the learning of a student [2] self such as by asking questions that prompt the student to monitor and evaluate (e.g., "What do you already know about 'if' blocks that would help here?''). In this way, thinking and reasoning through the problem is shared by the group members [2] self.

As computer science education opportunities for elementary students (grades K-5) are expanding, there is growing interest in using pair programming with these students. However, previous research findings do not fully support its use with younger learners, and some researchers have begun to examine whether introducing a second computer with a shared coding workspace can provide important benefits. This experience report describes a series of classroom activities in the 4th and 5th grades (ages 9-11 years old) with two different pair programming configurations: one-computer pair programming, in which both students share a keyboard, mouse, and monitor; and two-computer pair programming, in which each student has a separate computer but coding workspaces are synchronized over the web. In both cases the students sat next to each other and engaged in face-to-face conversation. We found that students largely preferred two-computer pair programming over one-computer pair programming. We conducted focus groups and transcribed collaborative dialogues to gain more insight into this preference. We learned that students felt more independence in two-computer pair programming, although they struggled with coordinating their edits with their partner. In one-computer pair programming, students reported not wanting to wait for their turn to drive, but feeling as though they communicated more with their partner. Both configurations can be productive for students, but the tradeoffs described in this experience report are important for CS educators and researchers to consider when determining which collaborative configuration to use in each K-5 classroom context.

The language and concepts used by curriculum designers are not always interpreted by children as designers intended. This can be problematic when researchers use self-reported survey instruments in concert with curricula, which often rely on the implicit belief that students’ understanding aligns with their own. We report on our refinement of a validated survey to measure upper elementary students’ attitudes and perspectives about computer science (CS), using an iterative, design-based research approach informed by educational and psychological cognitive interview processes. We interviewed six groups of students over three iterations of the instrument on their understanding of CS concepts and attitudes toward coding. Our findings indicated that students could not explain the terms computer programs nor computer science as expected. Furthermore, they struggled to understand how coding may support their learning in other domains. These results may guide the development of appropriate CS-related survey instruments and curricular materials for K–6 students.

We present a new framework describing how teachers use ST Math, a curriculum-integrated, year-long educational game, in 3rd-4th grade classrooms. We combined authentic classroom observations with teacher interviews to identify teacher needs and practices. Our findings extended and contrasted with prior work on teachers' behaviors around classroom games, identifying differences likely arising from a digital platform and year-long curricular integration. We suggest practical ways that curriculum-integrated games can be designed to help teachers support effective classroom culture and practice.

Extensive prior research suggests that pair programming holds many benefits for novices. Pair programming has been well studied at the undergraduate level, and recently, the CS education research community has started to realize that younger learners may also benefit from pair programming. However, an important factor in pair programming success for young learners is the ability to resolve conflicts during the process. Little is known about what types of conflicts occur while elementary students pair program or how those conflicts are, or are not, resolved. To investigate this phenomenon, we analyzed the videos of six pairs of students completing a programming activity. We found that conflicts evolve in four general stages, which may not all be present in each conflict: initiation, escalation, de-escalation, and conclusion. Some conflicts are resolved when the students come to an agreement, others end passively. The analysis revealed that the pairs' conflicts began around disagreements about code, who should have control of the keyboard and mouse, and other interpersonal events. This research indicates that conflicts are a significant concern for young students, and supporting young learners in developing improved collaboration skills is a key direction for CS education research.

Demand for K-6 computer science (CS) curricula is growing considerably. Many of the existing curricula have been developed by domain experts who are comfortable with specific and technical terminology, which they expect students to master. However, children are not always comfortable with these terms nor do they understand general concepts like 'coding' in the way that the curriculum designers intend. This is a problem because many researchers use self-report and attitudinal survey instruments with the implicit belief that the students' understanding of the terms and concepts resemble their own. This mismatch may invalidate results. For this project, we report on our modification of a validated survey to measure upper elementary students' attitudes about and perspectives on CS by attempting to understand the appropriate language to use when querying children about these topics. We use an iterative, design-based research approach that is informed by educational and psychological cognitive interview processes. We interviewed two groups (N=64) of upper elementary students on their understanding of computer science concepts and attitudes toward coding. Our findings indicate that 4th and 5th grade students could not explain the terms computer programs nor computer science as we had expected and that they struggled to understand how coding may connect with or support their learning in other domains. These results will help to guide the development of appropriate survey instruments and course materials for K-6 students, which both match their use of broad domain concepts and therefore inform their understanding and improve their outcomes.