Understanding how students reason about code is essential for providing tailored scaffolding in computer science (CS) education. Prior work has used think-aloud protocols with the Structure of the Observed Learning Outcomes (SOLO) taxonomy to examine students' code comprehension and programming levels. However, analyzing such data is labor-intensive and requires expert judgment. Recent advances in large language models (LLMs) offer a promising avenue for scaling this analysis, though their reliability for fine-grained coding remains uncertain. To address this gap, our study investigates the extent to which GPT-5 and 4o can classify SOLO levels and identify code-comprehension strategies from think-aloud transcripts of 27 high-school students working on block-based and text-based tasks. Results show modest alignment with human ratings for SOLO, with one-shot prompting improving agreement over zero-shot, though distinctions between adjacent lower levels (e.g., Prestructural 1 vs. 2) remained difficult. Strategy detection demonstrated stronger performance, achieving accuracies of 75–77% (block) and 62–67% (text), particularly for surface-visible strategies such as 'walkthroughs', 'control-structure identification', and 'pattern recognition', but weaker for less frequent, abstract, meta-cognitive strategies such as 'strategizing' (planning an approach) or 'thoroughness' (systematically checking work). These findings highlight both the potential and the limitations of using GPT-5 and 4o to analyze think-aloud data. While this work represents an initial step, with plans to examine more models, our preliminary results indicate that a human-in-the-loop approach is essential to ensure reliability and interpretive depth. Future work will extend this evaluation to other LLMs to better understand their role in supporting instructional decision-making.

Understanding how students comprehend code is essential for designing effective instructional support in computer science (CS). While prior studies have often relied on written responses, few have examined students' reasoning processes through think-aloud data. In this study, we analyzed the verbal reasoning of 27 high school students as they completed block-based and text-based code comprehension tasks targeting loops and conditional statements. Using an adapted SOLO taxonomy framework, we found that most students were classified at lower levels, with performance declining as they transitioned from block-based to text-based code. Students' strategy use, informed by prior work on code comprehension, showed that walkthroughs and identifying program structures were the most common approaches. Text-based tasks more often led students to use pattern-recognition strategies, such as interpreting operators or identifying numerical patterns, whereas block-based tasks occasionally prompted them to articulate broader problem-solving approaches. Overall, these findings demonstrate the value of applying the SOLO taxonomy to evaluate students' programming levels and highlight how programming modality impacts both the depth of understanding and the strategies students employ during code comprehension.

In this poster we highlight the strategies of K-12 in-service teachers who are learning to program in Java for the first time in an online graduate certificate program. Six teachers reported having limited and fundamental prior CS experience before learning to program in Java through a three-step Use-Modify-Create process. We implemented a model of metacognitive activity questionnaire to retrospectively collect a wide range of strategies reported by teachers as procedural steps which occurred before, during and after programming. Last, we interpret how these teachers' strategies were deployed as a higher-ordered process in the context of programming through a lens of abstraction and metacognitive regulation skills of planning, monitoring and evaluating the program solution.

This paper examines the co-design process for a foundational AI microcredential course targeting K-12 teachers' knowledge, agency, and effectiveness in integrating AI into their classrooms. We collaborated with six K-12 teachers and instructional coaches to ensure the course's relevance and practicality. Using conjecture mapping and memoing, we systematically captured and analyzed insights from the collaborative process. These methods helped us pinpoint essential themes and requirements for effective professional development (PD) that meets the unique challenges and opportunities of teaching about and using AI in K-12 classrooms. Themes included concerns about in-class monitoring for unethical impacts of AI integration and the desire for empowerment in evaluating and selecting AI tools that they can best leverage to meet state and national standards. Educator requirements centered on the creation of quick, easily accessible, and asynchronous learning activities. In addition, educators requested just-in-time AI integration resources and learning opportunities that can be leveraged throughout the year, rather than being limited to PD sessions. This study contributes to AI education by providing a framework for designing teacher professional development programs that are responsive to the evolving educational landscape and the specific needs of K-12 teachers.

The introduction of block-based coding provided teachers a new method to teach computer science to beginners. Since then, numerous programmable learning technologies (PLTs) utilizing this coding modality have surfaced, each bringing their unique properties to the CS education playing field. But while research comparing blocks to text-based coding have identified benefits and drawbacks of each, the same has not been done solely among block-based coding PLTs, specifically regarding their affordances to teachers' learning objectives and instructional needs. In this poster presentation, we present an experiment seeking to understand which properties of block-based coding environments teachers find most attractive to their pedagogical needs. Teachers enrolled in an online graduate-level CS pedagogy course reviewed 6 block-based coding PLTs, reflecting on the extent to which they would use the tools in their classes. We explain their assignment, guidelines given to scaffold their reviews, and our plan for future analysis.

The expansion of K-12 computer science education has led to an increasing effort in preparing in-service teachers to learn and teach CS through participation in micro credentials, professional development, and online certificate programs. In this study, we recruited five novice K-12 in-service teachers enrolled in an online CS certificate program and highlighted their strategies used in programming a Java assignment through a three-phase process of planning, monitoring and evaluation.

Despite widespread recognition of the importance of computer science (CS) education and an increased focus on computational thinking (CT) instruction in the U.S., there remains a significant shortage of qualified K-12 CS teachers. Preparing preservice teachers to teach CS is essential for ensuring a sustainable future for CS education. This experience report presents a CT/CS module initiative designed to prepare secondary preservice science and math teachers to teach CT/CS through an instructional methods course that integrates instructional practice within an elementary after-school program. This initiative employs the preservice teacher preparation framework that includes the observe-practice-reflect cycle. Within this framework, the preservice teachers began with incorporating unplugged lessons and then transitioned to robotics to teach CT/CS concepts. In this experience report, we detail the design and implementation of this CT/CS module initiative and share findings that revealed the preservice teachers' increased confidence, adaptations in lesson planning, and the challenges faced in teaching CT/CS. These takeaways aim to inspire and inform other teacher educators and practitioners working to prepare the next generation of CS teachers.

This demo introduces a hands-on robotics module designed to cultivate AI literacy in elementary students through computational thinking and robotics. Co-designed with 20 elementary school teachers and instructional coaches, the module helps students differentiate between artificial intelligence and human intelligence, focusing on the AI4K12 Big Idea of Perception, and specifically how our senses differ from a robot's sensors. The centerpiece of the curriculum is the "alien planet" task, where students program a robot to explore an unseen planet created by a peer, using its sensors to identify objects like "aliens" (green), "water" (blue), and "unique materials" (red). This activity not only builds critical thinking and problem-solving skills but also introduces foundational AI concepts, such as how machines perceive the world differently than humans. By connecting to other subjects like art (e.g., hue values) and linking to students' real-world experiences, the module offers a truly interdisciplinary and relevant approach to early AI education.

Music and computer science (CS) have profound historical and structural connections, with programming music offering a promising avenue for engaging children in CS through creative expression. To foster this engagement, our team developed M-Flow, a flow-based music programming platform designed to introduce students to CS via music. Despite extensive existing research in music and CS education, experience reports and empirical studies on K-12 teachers' implementation and its impact on young kids' learning are limited. Therefore, we recruit elementary school teachers and students with no or limited prior programming experience, introducing them to M-Flow and its curriculum through a professional development workshop, a semester's job embedded support, and classroom implementation. We describe the experiences of teachers as they attempt to integrate music and CS, the challenges they face, and the influence on students' attitudes toward learning computing concepts. Specifically, we reflect on our intervention by conducting a sequential mixed-method evaluation. During the qualitative phase, we collected multiple sources of data from three teachers through focus groups and debriefings after a semester of classroom implementation. Thematic analysis of workshop activities, interviews, and debrief videos revealed three themes with seven sub-themes on teachers' integration of flow-based music programming and two themes with five sub-themes on challenges faced by the teachers. In the quantitative phase, we gathered data on attitudes and self-efficacy from 75 students taught by these teachers. Results indicate that the flow-based music programming environment provided an engaging programming experience for students and significantly increased their self-efficacy towards learning programming.

As efforts to incorporate Computer Science (CS) and Computational Thinking (CT) into K-12 classrooms continue to expand, there is a growing need for programs that prepare teachers for the effective teaching and integration of CS and CT into their instruction. An ongoing challenge is preparing current and future teachers to develop the skills and confidence needed to teach and integrate CS and CT. Micro-credentials, designed as a short, focused course, offer opportunities for teachers to build skills and confidence through targeted study. This experience report examines a self-paced online micro-credential developed and implemented within a university-based college of education. The micro-credential was designed to equip both pre-service and in-service teachers with the skills and knowledge necessary to teach and integrate CS and CT into K-12 teaching and learning. We describe the micro-credential, including its structure, sequencing, and content. We then present an exploration of teachers' experiences in the micro-credential. Findings from surveys and CS autobiographies show increases in participants' attitudes, beliefs, and perceptions toward the conceptual and technical aspects of teaching CS, with a particular focus on designing clear and actionable plans for integration. The results from this study provide valuable insights for the development of future CS- and CT-focused micro-credentials.

Novice programmers often face challenges in designing computational artifacts and fixing code errors, which can lead to task abandonment and over-reliance on external support. While research has explored effective meta-cognitive strategies to scaffold novice programmers' learning, it is essential to first understand and assess students' conceptual, procedural, and strategic/conditional programming knowledge at scale. To address this issue, we propose a three-model framework that leverages Large Language Models (LLMs) to simulate, classify, and correct student responses to programming questions based on the SOLO Taxonomy. The SOLO Taxonomy provides a structured approach for categorizing student understanding into four levels: Pre-structural, Uni-structural, Multi-structural, and Relational. Our results showed that GPT-4o achieved high accuracy in generating and classifying responses for the Relational category, with moderate accuracy in the Uni-structural and Pre-structural categories, but struggled with the Multi-structural category. The model successfully corrected responses to the Relational level. Although further refinement is needed, these findings suggest that LLMs hold significant potential for supporting computer science education by assessing programming knowledge and guiding students toward deeper cognitive engagement.

Question-asking is a crucial learning and teaching approach. It reveals different levels of students' understanding, application, and potential misconceptions. Previous studies have categorized question types into higher and lower orders, finding positive and significant associations between higher-order questions and students' critical thinking ability and their learning outcomes in different learning contexts. However, the diversity of higher-order questions, especially in collaborative learning environments. has left open the question of how they may be different from other types of dialogue that emerge from students' conversations, To address these questions, our study utilized natural language processing techniques to build a model and investigate the characteristics of students' higher-order questions. We interpreted these questions using Bloom's taxonomy, and our results reveal three types of higher-order questions during collaborative problem-solving. Students often use "Why", "How" and "What If' questions to I) understand the reason and thought process behind their partners' actions: 2) explore and analyze the project by pinpointing the problem: and 3) propose and evaluate ideas or alternative solutions. In addition. we found dialogue labeled 'Social'. 'Question - other', 'Directed at Agent', and 'Confusion/Help Seeking' shows similar underlying patterns to higher-order questions, Our findings provide insight into the different scenarios driving students' higher-order questions and inform the design of adaptive systems to deliver personalized feedback based on students' questions.

All students benefit when computer science (CS) materials are accessible, but it is critical for students with disabilities. In order to provide opportunities for all students to be successful, it is important for teachers to be able to evaluate the accessibility of their lessons and technology. One way to evaluate accessibility is the POUR framework. The POUR framework represents what can be Perceived through the senses, how users can Operate a material or technology, how it is Understandable to users, and the overall Robustness. POUR provides a promising way for K-12 CS teachers to evaluate accessibility for their learners. We describe how the POUR framework was used by a cohort of teachers to evaluate VEX 123 for their learners with disabilities. Findings from the teacher POUR analysis revealed that overall, the teachers noted that the VEX 123 provided the necessary range of entryways into coding through its three modalities: The touch coding on the robot itself, the coder cards, and VECcode (the block-based coding environment). At the same time, the teachers indicated that some students with disabilities faced a number of motor and sensory difficulties. Overall, this study showcased a way for teachers to provide insight into the level of accessibility of CS education tools specific to their students' strengths and needs.

As our community seeks to make Computer Science education more accessible amidst a global mental health crisis and with many students living with chronic illnesses and other medical conditions, it becomes more and more urgent to address student health concerns in CS classes and in the spaces that we create. Despite the important and long overdue increase in the number of diversity, equity, inclusion, and accessibility (DEIA) initiatives in CS, these efforts run the risk of overlooking the needs of students living with ongoing mental health and medical conditions, who are underrepresented and marginalized in our field.

As conversational AI apps such as Siri and Alexa become ubiquitous among children, the CS education community has begun leveraging this popularity as a potential opportunity to attract young learners to AI, CS, and STEM learning. However, teaching conversational AI to K-12 learners remains challenging and unexplored due in part to the abstract and complex nature of some conversational AI concepts, such as intents and training phrases. One promising approach to teaching complex topics in engaging ways is through unplugged activities, which have been shown to be highly effective in fostering CS conceptual understanding without using computers. Research efforts are underway toward developing unplugged activities for teaching AI, but few thus far have focused on conversational AI. This experience report describes the design and iterative refinement of a series of novel unplugged activities for a conversational AI summer camp for middle school learners. We discuss learner responses and lessons learned through our implementation of these unplugged activities. Our hope is that these insights support CS education researchers in making conversational AI learning more engaging and accessible to all learners.

Educators should provide access to all students with inclusive and equitable computer science (CS) and computational thinking (CT) learning outcomes. Yet access to CS and CT is not always available for students with disabilities. This qualitative cross-case study examined the barriers and strengths three exemplar teachers faced, explored the supports and resources they provided, and presented how these teachers defined successful inclusive CS learning outcomes for their students. The data set included analysis of the semistructured teachers' interviews and teaching materials. The results included seven successful strategies the teachers identified: 1) using physical computing, 2) pair programming, 3)connecting CS and Individual Education Plans (IEP), 4)applying hands-on activities, 5) CT integration, 6) using CS vocabulary, and 7)open-ended pedagogy. Three resources and supports the teacher provided emerged from the data set as follows: 1) accessible instructional materials, 2) projects with multiple entry points, and 3) essential scaffolding supports. Whereas four barriers teachers faced; 1. subject matter, 2) accessible tools, 3) students receiving support, 3) the role of CS in instruction, and 4) the role of time served as additional findings. The results suggested that school-based practitioners, including administrators, can overcome the barriers and promote successful strategies that lead to asset-based CS-inclusion in the classroom.

As artificial intelligence permeates our lives through various tools and services, there is an increasing need to consider how to teach young learners about AI in a relevant and engaging way. One way to do so is to leverage familiar and pervasive technologies such as conversational AIs. By learning about conversational AIs, learners are introduced to AI concepts such as computers’ perception of natural language, the need for training datasets, and the design of AI-human interactions. In this experience report, we describe a summer camp curriculum designed for middle school learners composed of general AI lessons, unplugged activities, conversational AI lessons, and project activities in which the campers develop their own conversational agents. The results show that this summer camp experience fostered significant increases in learners’ ability beliefs, willingness to share their learning experience, and intent to persist in AI learning. We conclude with a discussion of how conversational AI can be used as an entry point to K-12 AI education.

Decomposition is a foundational computational thinking construct that is often introduced early as students are learning computer science in the elementary grades. Although decomposition is often described in early computational activities, little research exists about how to teach and assess students’ understanding of decomposition. In this mixed-methods research study, 173 third-grade students from eight elementary school classrooms in the Midwest were taught eight lessons that integrated decomposition as well as other computational thinking practices into their mathematics instruction. They completed a computational thinking assessment after the first four lessons and again after the second four lessons. Analyses included the distribution of correct decomposition item responses, confirmatory factor analysis, and item-level error analysis. Results indicate wide variability in students’ performance on the decomposition assessment items as well as in performance on items contextualized within mathematics. This study highlights the need for additional considerations about assessing computational understanding, implications for assessment within integrated contexts, and the use of paper-and-pencil tests compared to embedded assessments.

Women and people from historically marginalized groups are recognized as underrepresented in computer science (CS) courses. We approach this issue by building a K-12 teacher workforce as role models to K-12 students. In this project, we develop a K-12 CS Educator Certificate Program and provide scholarships to 20 Black, Latina, and Native American women in-service teachers. To support these scholars, we provided a community of practice that includes mentoring from experienced CS educators and flexible tutoring from CS content experts. Through these efforts to increase diversity and representation in those who teach CS, we are making strides toward all students seeing themselves in the field of CS.

To address the issue of meaningfully including students in elementary computer science (CS) education, we design, implement, and evaluate an innovative professional development (PD) and community of practice (CoP) model for supporting elementary general and special education teacher dyads to apply inclusive pedagogy in their CS instructional practice. This project aims to improve interest, ability beliefs, and academic outcomes for elementary students with disabilities in CS learning. This online PD/CoP supports teachers in learning, implementing, and reflecting on how to best integrate Universal Design for Learning and High-Leverage Practices within CS instruction. Membership in this PD/CoP is predicted to increase the participation of students with disabilities in elementary CS instruction by increasing the teachers' CS content and pedagogical knowledge, self-efficacy, and inclusive mindsets and practice.

The ubiquity of AI-based conversational apps such as Siri, Alexa and Google Assistant means more young users are interacting with these apps. The increasing popularity of these conversational applications brings a potential opportunity to attract learners to AI, CS and STEM fields. CS Education researchers need to explore how to leverage this opportunity, in particular to serve learners who are underrepresented in CS and STEM. This experience report describes the design and iterative refinement of a series of two-week summer camps in which 62 predominantly Black students participated in hands-on AI-based learning experiences to design and develop their own conversational AI apps. We discuss the organization of this summer camp experience, including strategies for recruiting from and building trust within the target community, designing professional development for camp facilitators, structuring the camp activities, and encouraging projects that are personally and socially relevant. We share challenges and lessons learned from this AI summer camp in the hopes that they will inform other researchers and practitioners who are interested in designing and deploying similar experiences.

Intelligent virtual agents have tremendous potential for facilitating collaborative learning by modeling and reinforcing desirable collaborative practices. Despite recent work in this area, the extent to which intelligent virtual agents can facilitate improvements in the collaborative behavior of children is largely unknown. This study employed a wizard-of-oz study design and investigated elementary children's collaborative behavior after interacting with virtual agents. These agents model exploratory talk for upper elementary school dyads, such as asking higher-order questions and listening to their partners. The findings uncover associations between elementary learner dyads' positive changes in collaboration after agent interventions, the dyads' affective reactions to interventions, and their attentiveness to the agents. Our results also reveal associations between positive changes in collaboration and the timing of interventions: for example, earlier interventions had a higher occurrence of positive changes, and positive changes in collaboration typically happened within five seconds of interventions. The results suggest ways in which intelligent virtual agents may be used to promote effective collaborative learning practices for children.

INFACT aims to provide differentiable teaching and learning activities in Computational Thinking (CT) that are inclusive for neurodiverse learners in grades 3-8. Neurodiversity refers to learners with autism, ADHD, dyslexia, and other related cognitive differences. Underlying many neurodiverse conditions is differences with executive function (EF). In previous research on game-based learning, neurodiverse students became class leaders in CT activities [1]. Special education teachers noted that CT practices align with strategies they use to support problem-solving practices. Leveraging these affordances of CT, INFACT activities are designed with embedded EF scaffolds to support neurodiverse learners.

Variable is a common computer science (CS) concept and is being introduced to upper elementary students in computational thinking (CT)-integrated instruction. However, there is scant empirical evidence of when and how elementary students should learn variables. For example, national computer science (CS) standards advise introducing variables in grades 3-5 and a K-8 variable learning trajectory (LT) synthesized learning goals from the literature and hypothesized four levels of thinking in working with variables. Yet, little empirical research lies behind these. This mixed methods study examined elementary students' understanding of variables. Participants were sampled from two fourth-grade classes from a Midwestern elementary school that implemented a series of CT-integrated math lessons. Students' written responses to variables assessment items were analyzed. Additionally, cognitive think-aloud interviews were conducted with nine students to elicit students' understanding while solving the variables assessment items. Our findings suggested that most students lacked a conceptual understanding of using variables to create generalized problem solutions that could work with any set of inputs. Additionally, students had difficulty with specific mechanics of using variables such as storing user input in a variable, updating variable values, and using the values stored in variables. This study underscores the need for careful design, use, and analysis of elementary CT-integrated lessons and assessments to introduce and reinforce the conceptual understanding and specific mechanics of variables for elementary students.

Discussion forums are invaluable resources when scaling up undergraduate CS courses to larger class sizes. However, passive incorporation of discussion forums is not a silver bullet, as these platforms tend to devolve into places of shallow engagement. To aid our understanding of the factors that influence the nature of these interactions, we collected data from three CS1/CS2 forums. We obtained survey responses from the course instructors and performed a content analysis of the question-response pairs across all the courses. The results suggest that students' help-seeking patterns are influenced by the course curriculum, mode of delivery, and the existence of other help-seeking avenues. The findings also shed light on common strategies used by instructors to incentivize productive student-teaching staff and student-student interactions (e.g., instructing students to describe their debugging questions in detail, asking teaching staff to respond with hints/questions instead of direct answers). This poster presents a series of takeaways that can inform CS educators' choices around discussion forums.

While computational thinking has gained popularity in K-12 schools to increase access to computing tools and practices, there is still limited understanding on how to broaden participation of students with disabilities in computational thinking (CT). One approach to increasing access to computing to students with disabilities is to educate future special education teachers to bring CT into their instruction. This study examined the influence of integrating CT into assistive technology course for special education pre-service teachers. Our results suggest that integrating CT into special educa- tion teacher preparation coursework can have a positive impact on how pre-service teachers see the value of bringing computational practices to students with disabilities.

More young people are interacting with smart conversational agents such as Alexa and Google Assistant. These platforms are extensible, providing, in principle, a compelling opportunity for young users to create and tinker with their own conversational agents. However, to date the interfaces for conversational app development are adult-focused. This paper presents the early design process for AMBY (AI Made by You), which we are building to empower young learners to create their own conversational agents. We first conducted a contextual inquiry with 14 middle school students (aged 11-13) in an AI summer camp, followed by two other usability studies. The system design has been refined after each study. Key features of AMBY include a visual dialogue management panel, testing panel with a diverse avatar, and a voice input modality. AMBY is designed to serve as a pedagogically-robust resource for K-12 AI education and as an engaging and creative way for middle schoolers to explore AI.

Through a mixed-methods approach that utilized teacher surveys and a focus group with computer science (CS) instructional coaches, this study examined elementary teachers’ confidence in meeting the needs of students with disabilities, the extent to which the teachers could use the Universal Design for Learning (UDL) framework in CS education, and the strategies that their CS instructional coaches used with them to help meet the needs of all learners, including those with disabilities. Findings from a Wilcoxon signed-rank test and a general linear regression of the teacher surveys revealed that teachers’ confidence in teaching CS and in meeting the needs of students with disabilities increased over the 5 month coaching study, but their understanding of UDL remained low throughout the study. A qualitative thematic analysis of open-response survey questions revealed that the teachers could identify instructional strategies that support the inclusion of students with disabilities in CS instruction. These strategies aligned with high leverage practices (HLPs) and included modeling, the use of explicit instruction, and opportunities for repeated instruction. When asked to identify UDL approaches, however, they had more difficulty. The focus group with coaches revealed that the coaches’ primary aim related broadly to equity and specifically to access to and the quality of CS instruction. However, although they introduced UDL-based strategies, they struggled to systematically incorporate UDL into coaching activities and did not explicitly label these strategies as part of the UDL framework on a consistent basis. This finding explains, to a large extent, the teachers’ limited understanding of UDL in the context of CS education.

There is little empirical research related to how elementary students develop computational thinking (CT) and how they apply CT in problem-solving. To address this gap in knowledge, this study made use of learning trajectories (LTs; hypothesized learning goals, progressions, and activities) in CT concept areas such as sequence, repetition, conditionals, and decomposition to better understand students’ CT. This study implemented eight math-CT integrated lessons aligned to U.S. national mathematics education standards and the LTs with third- and fourth-grade students. This basic interpretive qualitative study aimed at gaining a deeper understanding of elementary students’ CT by having students express and articulate their CT in cognitive interviews. Participants’ ( n = 22) CT articulation was examined using a priori codes translated verbatim from the learning goals in the LTs and was mapped to the learning goals in the LTs. Results revealed a range of students’ CT in problem-solving, such as using precise and complete problem-solving instructions, recognizing repeating patterns, and decomposing arithmetic problems. By collecting empirical data on how students expressed and articulated their CT, this study makes theoretical contributions by generating initial empirical evidence to support the hypothesized learning goals and progressions in the LTs. This article also discusses the implications for integrated CT instruction and assessments at the elementary level.

This basic interpretive qualitative study investigated four students’ debugging behaviors in Zoombinis, a game-based computational thinking (CT) environment. Analysis involved deductive coding of students’ debugging behaviors using videos of students’ computer screens. The findings revealed a range of debugging behaviors and strategies. Findings also indicated that students could articulate an intermediate understanding of debugging as related to the debugging LT [7].

Developing computational thinking (CT) assessment methods appropriate for elementary students is attracting growing attention as CT research in elementary education progresses. To review the current elementary CT assessments for potential gaps, and seek additional methodologies to expand our understanding of CT, an integrative literature review of 75 research papers was performed in two phases. In Phase One, we conducted a critical analysis of existing elementary CT assessment studies. Key results include: 1) Artifact analysis, CT assessment items, and interviews are the most common methods utilized to assess CT in elementary grades; 2) Existing CT assessments primarily focus on students' computational artifacts and performance on CT tests; however, strategies to study students' thought processes during CT problem-solving are limited and under-utilized. Guided by the results of phase one, along with the theoretical perspective that connected CT to visual processing ability, in phase two we performed a survey of literature in the area of understanding cognitive processes through eye-tracking (i.e., visual attention) and think-aloud methodologies (i.e., verbalization). We focused on eye-tracking and think-aloud methodologies as these have been used to understand students' cognitive processes during problem-solving in other areas. Based on these findings, we proposed that in addition to current established methodologies, eye-tracking with the think-aloud technique can provide new insights into students' CT.

Elementary schools throughout the United States are attempting to integrate computational thinking (CT) into their instruction, often without guidance from research about effective approaches for achieving particular CT goals. This cross-case study investigated the school-wide integration of CT in three elementary schools in a large urban school district in the Northeast that has a district-led CS for All initiative. Data included interviews with teachers, professional development providers, and school administrators as well as surveys from teachers and classroom observations in each participating school. Findings revealed three distinct approaches to integration: (a) single teacher leader-driven model, (b) scaffolded professional development model, and (c) intensive coaching model. These approaches reflect the visions set by administrators and teachers, methods used by professional development providers, and cultures of each school. Across the case studies, common pedagogical approaches included strategic use of both unplugged and plugged activities with a range of computational tools, a focus on collaborative project-based learning, and the use of CT-specific academic language to anchor new CT learning within the academic disciplines. The study highlighted advantages and challenges within each integration approach with implications for schools considering CT integration.

The computer science (CS) education field is exploring several instructional strategies for teaching CS to children in elementary school. Strong arguments have been made for integration--- constructing activities that not only teach CS, but use the CS to support learning in a core subject. Integrating CS materials into a specific curriculum is a non-trivial task that may unfairly burden elementary teachers, who are often generalists. Successful development and classroom implementation of integrated materials relies on many decisions about what, when, and how much subject matter to cover in relation to the main curriculum.

Debugging has been an expanding topic in K-12 computer science (CS) education research. However, few studies have focused on in-depth analysis of elementary students' debugging in block-based visual programming environments. Thus, using the video analysis technique, this basic interpretive qualitative study aimed to explore what debugging behaviors students exhibited and how these debugging behaviors mapped with an existing K-8 debugging learning trajectory (LT). Findings revealed five types of debugging behaviors and four primary challenges. These debugging behaviors mapped to five consensus goals in the K-8 debugging learning trajectory. Future research will focus on students' efficiency in using debugging strategies and understanding of debugging.

Given the academic diversity of today's classrooms, elementary teachers engaged in computer science (CS) and computational thinking (CT) instruction must create CS/CT experiences that are accessible and engaging to a broad range of learners, including those with disabilities. One method of developing inclusive instructional experiences is through the Universal Design for Learning (UDL) framework, wherein teachers proactively design instruction for the broadest range of learners. Doing so may be challenging as elementary teachers may not be familiar with the UDL framework or may not have experience with applying UDL within CS/CT instruction. The purpose of this qualitative study was to investigate how four elementary teachers provided UDL-based instruction to academically diverse learners during CS/CT instruction. Teachers received professional development and instructional coaching related to UDL within CS/CT education. Data included teachers' lesson plans, coaching logs, and teacher interviews which were qualitatively analyzed and triangulated. Data revealed that teachers generally addressed all three UDL principles, with an emphasis on two of the principles (multiple means of engagement and multiple means of representing content) above the third principle (multiple means of action and expression). They focused on breaking tasks into steps, emphasizing student choice, and presenting information in multiple ways. Findings revealed nuanced implementation differences among the teachers as well.

This study aimed to understand elementary students' challenges and interactions in computational thinking-integrated botany through robotics activities. Data was collected from screen-casting videos and analyzed using Collaborative Computing Observation Instrument (CCOI), a web-based analysis instrument with nodes and paths that classify and specify students' computing experience. The results revealed that all participants engaged in independent work for most of the time, with short interactions on 1) general computer technology issues; 2) software navigating issues; 3) questions about academic content; 4) computing discussion with the instructor; 5) informing the instructor about task accomplishment. The findings of this study will provide important insights to CS researchers, educators, and elementary teachers regarding CT-integration research and practice.

For K-8 computer science (CS) education to continue to expand, it is essential that we understand how students develop and demonstrate computational thinking (CT). One approach to gaining this insight is by having students articulate their understanding of CT through cognitive interviews. This study presents findings of a cognitive interview study with 13 fourth-grade students (who had previously engaged in integrated CT and mathematics instruction) working on CT assessment items. The items assessed four CT concepts: sequence, repetition, conditionals, and decomposition. This study analyzed students\textquotesingle articulated understanding of the four CT concepts and the correspondence between that understanding and hypothesized learning trajectories (LTs). We found that 1) all students articulated an understanding of sequence that matched the intermediate level of the Sequence LT; 2) a majority of students\textquotesingle responses demonstrated the level of understanding that the repetition and decomposition items were designed to solicit (8 of 9 responses were correct for repetition and 4 of 6 were correct for decomposition); and 3) less than half of students\textquotesingle responses articulated an understanding of conditionals that was intended by the items (4 of 9 responses were correct). The results also suggested questioning the directional relationships of two statements in the existing Conditionals LT. For example, unlike the LT, this study revealed that students could understand "A conditional connects a condition to an outcome'' before "A condition is something that can be true or false.''

This study investigated school-wide integration of computational thinking (CT) in elementary schools of: 1) systems-level approaches to integration; 2) teachers' understanding and implementation of CT integration, and 3) challenges to integration. Data sources include interviews with teachers, professional development (PD) providers, principals as well as implementation observations. Findings revealed three distinct approaches: (a) Lone STEM teacher implementer, (b) PD scaffolded approach, and (c) Whole school coach-in-residence approach. Teachers generally viewed CT in the context of problem-solving. Although struggles and challenges existed in all three schools, administrators, PD providers, and teachers all had a high commitment to CT integration.

Computer science (CS) education is rapidly expanding in the United States[4]. That said, the CS education field is still grappling with coming to consensus about definitions of K-12 CS and how to reach all students. While the CS education community has made great efforts to expand opportunity for under served groups, students with disabilities have regularly been left out of the conversation. According to the National Center for Education Statistics, approximately 13% of all students enrolled in public schools in the US receive special education services and 95% of these students are taught either part or full time in the regular classroom[3] . One aim of CSforALL is to increase equity in CS education and opportunities[5]. Recent studies have examined the challenges faced by students with disabilities in K12 CS education[1][2]. Including students with disabilities in CS classes not only increases their access to academic and career opportunities in CS, but it also gives them the opportunity to develop new ways of thinking and participating in the world that they would otherwise be potentially without. This panel addresses the inclusion of students with disabilities as part of the national all and seeks to augment the discussion initiated by the CSforALL Consortium and AccessCSforALL with the introduction of the Accessibility Pledge at the annual CSforALL Summit. This panel brings together four different experts, with a wide range of experience in regards to computer science education and students with disabilities, in an effort to expand both the national conversation and increase efforts related to including students with disabilities equitably in CS education. In this panel we present a group of CS education community members who represent multiple approaches to accessibility and serving students with disabilities, as well as diverse implementations; peer-to-peer mentoring, initiatives focused on a single subpopulation of students with disabilities, curriculum and platform providers, and district and state-wide solutions. The panelists, and the organizations they represent have a diversity of experiences to share, including current high school students and parents of students with disabilities.

Now more than ever, students with disabilities are participating in computer science (CS) education. As CS increasingly becomes a part of the general curriculum in grades K-8, ensuring that these learning experiences are equitable and accessible for a wide range of learners may help broaden the diversity of individuals who choose to engage in computing experiences throughout their schooling and into their professional lives (Qualls & Sherrell, 2010). Therefore, it is essential to identify pedagogical approaches that lower barriers for students with disabilities and give teachers new tools to help those students succeed. Initial findings will be presented from a National Science Foundation STEM+C project derived from a series of qualitative case studies about challenges faced by K-8 students with disabilities in CS education. These findings have been converted to practitioner-oriented pedagogy briefs, written in the form of vignettes, and grounded in our own research findings as well as special education best practice. These pedagogy briefs are disseminated to practitioners and used for professional development and intervention work. Current topics include: Universal Design for Learning, project planning, co-teaching, working with paraeducators, and promoting student collaboration. Pedagogy briefs will be available and can be found on the Creative Technology Research Lab (CTRL) website: http://ctrl.illinois.education.edu

Despite the proliferation of K-12 computer science (CS) programs and implementation of "CS for All" initiatives in U.S. schools, little research has been conducted on effective pedagogical approaches in K-12 CS. Even less research has focused on meeting the needs of students with disabilities. This paper presents findings from a qualitative case study examining the experiences of teachers who taught CS classes that included students with disabilities. The goal of this study was to identify pedagogical approaches that the teachers used to meet the needs of all students. Results indicated that teachers implemented three primary instructional strategies to address the needs of students with disabilities including facilitating student collaboration, using the Universal Design for Learning (UDL) framework, and using explicit instruction to teach CS concepts.

As CS for All initiatives expand in K-12 districts across the country, there is a need to create ongoing teacher support and training. Cornell Tech's Teacher in Residence program builds computational agency. Agency is often defined as the power to freely act and make choices. The Teacher in Residence program seeks to build the agency of school administrators, teachers, and students to make choices about CS education and to act on them based on a foundation of content knowledge rather than programs bound to specific tools or individuals. The Teacher in Residence program is grounded in evidence-based practices, but has made unique adaptations in order to support teachers who are new to CS content. The writers will share practices, learnings, and preliminary outcomes from the first year and a half of the program. His data includes qualitative measures of teacher confidence, agency, and accuracy as well as initial data on student engagement and generalization. The Teacher in Residence program includes K-8 teachers who are incorporating CS instruction into their classrooms. In this program, a master teacher is embedded in a school community for a limited amount of time to coach teachers, offer professional development, and consult with the administration about implementation. It focuses on three elements: content proficiency, appropriate pedagogy, and giving equitable access to all students. Handouts will be provided.

Despite efforts to integrate computer science (CS) into K-12 education, there are numerous unanswered questions about how students learn CS, how to provide positive computing experiences, and how students interact with each other during CS instruction. To begin to deconstruct these complexities for a diverse range of students, it is important to not only study the outcomes and products of students' computational experiences, but also the processes they take in creating those products. In recognizing the necessity for targeted, narrow research questions, this paper focused on how elementary students interacted with each other during puzzle-based CS instruction. Future work will focus on comparing these findings to students' collaborative interactions in more open-ended computing situations. Data analysis made use of the Collaborative Computing Observation Instrument (C-COI) [M. Israel et al. 2015] to analyze video screen captures of nine students as they engaged in CS activities within Code.org's Code Studio. Findings confirmed three predominant types of collaborative interactions: Collaborative problem solving, excitement and accomplishment related to CS activities, and general socialization.

There is growing momentum to integrate computer science (CS) education across K-12, but there is little information about how this integration should take place (Grover & Pea, 2013). This is especially true in the elementary grades, as fewer studies have examined computing at these grades. Through a National Science Foundation STEM+C project, we are developing and studying learning progressions for integrated CS and mathematics at the elementary level. Our research examines how teachers are introducing CS concepts within mathematic as well as what computational concepts and practices naturally can be taught within the context of elementary mathematics. We are also examining how these emerging progressions align with the K-12 CS Framework and the new standards from the Computer Science Teachers Association (CSTA). Future aims are to develop a coherent set of learning progressions related to areas such as debugging, sequencing, looping, conditionals, and decomposition within mathematics topics such as geometry, fractions, and arithmetic number stories. Our research lays the groundwork for the development of learning trajectories that will guide curriculum developers and practitioners to understand how to teach students across grades K-5 computing within the context of their mathematics instruction.

As awareness of computer science education grows in the general public, it is important to showcase computer science education as accessible for all grades K-12 and beyond. As panelists present the projects and research they've been conducting, we will highlight three overarching topics: