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.

The "Computer Science for All" initiative advocates for universal access to computer science (CS) instruction. A key strategy toward this end has been to establish CS content standards outlining what all students should have the opportunity to learn. Standards can support curriculum quality and access to quality CS instruction, but only if they are used to inform curriculum design and instructional practice. Professional learning offered to teachers of CS has typically focused on learning to implement a specific curriculum, rather than deepening understanding of CS concepts. We set out to develop a set of educative resources, formative assessment tools and teacher professional development (PD) sessions to support middle school CS teachers' knowledge of CS standards and standards-aligned formative assessment literacy. Our PD and associated resources focus on five CS standards in the Algorithm and Programming strand and are meant to support teachers using any CS curriculum or programming language. In this experience report, we share what we learned from implementing our standards-based PD with four middle school CS teachers. Teachers initially perceived standards as irrelevant to their teaching but they came to appreciate how a deeper understanding of CS concepts could enhance their instructional practice. Analysis of PD observations and exit surveys, teacher interviews, and teacher responses to a survey assessing CS pedagogical content knowledge demonstrated the complexity of using content standards as a driver of high-quality CS instruction at the middle school level, and reinforced our position that more standards-focused PD is needed.

The development of the CS content standards underscores the importance of curricula aligned with the standards, ensuring equitable coverage of CS concepts for all students. Because standards are broad, we emphasize the need for CS curricula to specify not only the standards they align with but also which aspects of the standards they align with and how. We map one common middle school CS curriculum to a few standards to demonstrate this need.

The rapid expansion of K-12 CS education has made it critical to support CS teachers, many of whom are new to teaching CS, with the necessary resources and training to strengthen their understanding of CS concepts and how to effectively teach CS. CS teachers are often tasked with teaching different curricula using different programming languages in different grades or during different school years, and tend to receive different professional development (PD) for each curriculum they are required to teach. This often leads to a lack of deep understanding of the underlying CS concepts and how different curricula address the same concepts in different ways. Empowering teachers to develop a deep understanding of CS standards, and use formative assessments to recognize common student challenges associated with the standards, will enable teachers to provide more effective CS instruction, irrespective of the curriculum and/or programming language they are tasked with using. This position paper advocates supporting CS teacher professional learning by supplementing existing curriculum-specific teacher PD with standards-aligned PD that focuses on teachers' conceptual understanding of CS standards and ability to adapt instruction based on student understanding of concepts underlying the CS standards. We share concrete examples of how to design standards-aligned educative resources and instructionally supportive tools that promote teachers' understanding of CS standards and common student challenges and develop teachers' formative assessment literacy, all essential components of CS pedagogical content knowledge.

The K-12 CS Framework provides guidance on what concepts and practices students are expected to know and demonstrate within different grade bands. For these guidelines to be useful in CS education, a critical next step is to translate the guidelines to explicit learning targets and design aligned instructional tools and assessments. Our research and development goal in this paper is to design a playful, curriculum-neutral assessment aligned with the 'Data and Analysis' concept (grades 6-8) from the CS framework. Using Evidence Centered Design and Participatory Design, we present a set of assessment guidelines for assessing data and analysis, as well as a set of design considerations for integrating data and analysis across middle school curricula in CS and non-CS contexts. We outline these contributions, describe how they were applied to the development of a game-based formative assessment for data and analysis, and present preliminary findings on student understanding and challenges inferred from student gameplay.

In today's increasingly digital world, it is critical that all students learn to think computationally from an early age. Assessments of Computational Thinking (CT) are essential for capturing information about student learning and challenges. Several existing K-12 CT assessments focus on concepts like variables, iterations and conditionals without emphasizing practices like algorithmic thinking, reusing and remixing, and debugging. In this paper, we discuss the development of and results from a validated CT Practices assessment for 4th-6th grade students. The assessment tasks are multilingual, shifting the focus to CT practices, and making the assessment useful for students using different CS curricula and different programming languages. Results from an implementation of the assessment with about 15000 upper elementary students in Hong Kong indicate challenges with algorithm comparison given constraints, deciding when code can be reused, and choosing debugging test cases. These results point to the utility of our assessment as a curricular tool and the need for emphasizing CT practices in future curricular initiatives and teacher professional development.

Free-choice, open-ended projects are commonly used to assess student learning in introductory block-based programming (BBP) environments. They are generally assessed in school based on criteria such as the social impact conveyed, whether the projects work without errors, and whether they are creative and engaging. Additionally, researchers have assessed such projects based on the frequency of use of various coding constructs like variables, conditionals, and iterations. This paper presents a novel multi-dimensional rubric for analyzing open-ended BBP projects that integrates assessment of front-end project design and back-end sophistication of use of coding constructs. Further, the novelty of the rubric lies in the fact that instead of relying solely on frequencies, it uses scaled scores based on sophistication of rubric components. Using this rubric, 160 Scratch and App Inventor projects were scored and analyzed. The paper establishes external validity of the rubric and examines what we can learn about student learning from this analysis. Our findings will help K-12 CS educators and curriculum developers recognize what aspects of CS middle school students need most support on, and how to leverage programming environments to provide this support.

C2STEM is a web-based learning environment founded on a novel paradigm that combines block-structured, visual programming with the concept of domain specific modeling languages (DSMLs) to promote the synergistic learning of discipline-specific and computational thinking (CT) concepts and practices. Our design-based, collaborative learning environment aims to provide students in K-12 classrooms with immersive experiences in CT through computational modeling in realistic scenarios (e.g., building models of scientific phenomena). The goal is to increase student engagement and include inclusive opportunities for developing key computational skills needed for the 21st century workforce. Research implementations that include a semester-long high school physics classroom study have demonstrated the effectiveness of our approach in supporting synergistic learning of STEM and CS/CT concepts and practices, especially when compared to a traditional classroom approach. This technology demonstration will showcase our CS+X (X = physics, marine biology, or earth science) learning environment and associated curricula. Participants can engage in our design process and learn how to develop curricular modules that cover STEM and CS/CT concepts and practices. Our work is supported by an NSF STEM+C grant and involves a multi-institutional team comprising Vanderbilt University, SRI International, Looking Glass Ventures, Stanford University, Salem State University, and ETR. More information, including example computational modeling tasks, can be found at C2STEM.org.

We demonstrate that, by using a small set of hand-graded student work, we can automatically generate rubric criteria with a high degree of validity, and that a predictive model incorporating these rubric criteria is more accurate than a previously reported model. We present this method as one approach to addressing the often challenging problem of grading assignments in programming environments. A classic solution is creating unit-tests that the student-generated program must pass, but the rigid, structured nature of unit-tests is suboptimal for assessing the more open-ended assignments students encounter in introductory programming environments like Alice. Furthermore, the creation of unit-tests requires predicting the various ways a student might correctly solve a problem - a challenging and time-intensive process. The current study proposes an alternative, semi-automated method for generating rubric criteria using low-level data from the Alice programming environment.

Block-based programming environments such as Scratch, App Inventor, and Alice are a key part of introductory K-12 computer science (CS) experiences. Free-choice, open-ended projects are encouraged to promote learner agency and leverage the affordances of these novice-programming environments that also support creative engagement in CS. This mixed methods research examines what we can learn about student learning from such programming artifacts. Using an extensive rubric created to evaluate these projects along several dimensions, we coded a sample of ~80 Scratch and App Inventor projects randomly selected from 20 middle school classrooms in a diverse urban school district in the US. We present key elements of our rubric, and report on noteworthy trends including the types of artifacts created and which key programming constructs are or are not commonly used. We also report on how factors such as students' gender, grade, and teachers' teaching experience influenced students' projects. We discuss differences between programming environments in terms of artifacts created, use of computing constructs, complexity of projects, and use of features of the environment for creativity, interactivity, and engagement. Our findings will help educators of introductory computing be more cognizant of how best to leverage the programming environments they are using, and what aspects they need to focus on as they attempt to address the learning needs of all in "CS For All."

Many introductory programming environments generate a large amount of log data, but making insights from these data accessible to instructors remains a challenge. This research demonstrates that student outcomes can be accurately predicted from student program states at various time points throughout the course, and integrates the resulting predictive models into an instructor dashboard. The effectiveness of the dashboard is evaluated by measuring how well the dashboard analytics correctly suggest that the instructor help students classified as most in need. Finally, we describe a method of matching low-performing students with high-performing peer tutors, and show that the inclusion of peer tutors not only increases the amount of help given, but the consistency of help availability as well.

K-12 classrooms use block-based programming environments (BBPEs) for teaching computer science and computational thinking (CT). To support assessment of student learning in BBPEs, we propose a learning analytics framework that combines hypothesis- and data-driven approaches to discern students' programming strategies from BBPE log data. We use a principled approach to design assessment tasks to elicit evidence of specific CT skills. Piloting these tasks in high school classrooms enabled us to analyze student programs and video recordings of students as they built their programs. We discuss a priori patterns derived from this analysis to support data-driven analysis of log data in order to better assess understanding and use of CT in BBPEs.

Programming in block-based environments is a key element of introductory computer science (CS) curricula in K-12 settings. Past research conducted in the context of text-based programming points to several challenges related to novice learners' understanding of foundational programming constructs such as variables, loops, and expressions. This research aims to develop assessment items for measuring student understanding in introductory CS classrooms in middle school using a principled approach for assessment design. This paper describes the design of assessments items that were piloted with 100 6th, 7th, 8th graders who had completed an introductory programming course using Scratch. The results and follow-up cognitive thinkalouds indicate that students are generally unfamiliar with the use of variables, and harbor misconceptions about them. They also have trouble with other aspects of introductory programming such as how loops work, and how the Boolean operators work. These findings point to the need for pedagogy that combines popular constructionist activities with those that target conceptual learning, along with better professional development to support teachers' conceptual learning of these foundational constructs.

Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students’ learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students’ CT skills. Informed by past literature and based on our empirical work examining a dataset from the use of the Fairy Assessment in the Alice programming environment in middle schools, we present a framework that formalizes a process where a hypothesis-driven approach informed by Evidence-Centered Design effectively complements data-driven learning analytics in interpreting students’ programming process and assessing CT in block-based programming environments. We apply the framework to the design of Alice tasks for high school CS to be used for measuring CT during programming.

Computational Thinking (CT) is considered a core competency in problem formulation and problem solving. We have developed the Computational Thinking using Simulation and Modeling (CTSiM) learning environment to help middle school students learn science and CT concepts simultaneously. In this paper, we present an approach that leverages multiple linked representations to help students learn by constructing and analyzing computational models of science topics. Results from a recent study show that students successfully use the linked representations to become better modelers and learners.

Computational thinking (CT) draws on fundamental computer science concepts to formulate and solve problems, design systems, and understand human behavior. CT practices (e.g., problem representation, abstraction, decomposition, simulation, verification, and prediction) are also central to the development of expertise in a variety of STEM disciplines. Exploiting this synergy between CT and STEM disciplines, we have developed CTSiM, a cross-domain, scaffolded, visual-programming and agent-based learning environment for middle school science. We present and justify the CTSiM architecture and its implementation. To identify challenges and scaffolding needs in learning with CTSiM, we present a case study describing the challenges that a highand a low-achieving student faced while working on kinematics and ecology units using CTSiM. Decreases in the number of challenges for both students over sequences of related activities illustrate the combined effectiveness of our approach. Further, the specific challenges and scaffolds identified suggest the design of an adaptive scaffolding framework to help students develop a synergistic understanding of CT and science concepts.

Computational Thinking (CT) defines a domain-general, analytic approach to problem solving that combines concepts fundamental to computing, with systematic representations for concepts and problem-solving approaches in scientific and mathematical domains. We exploit this trade-off between domain-specificity and domain-generality to develop CTSiM (Computational Thinking in Simulation and Modeling), a cross-domain, visual programming and agent-based learning environment for middle school science. CTSiM promotes inquiry learning by providing students with an environment for constructing computational models of scientific phenomena, executing their models using simulation tools, and conducting experiments to compare the simulation behavior generated by their models against that of an expert model. In a preliminary study, sixth-grade students used CTSiM to learn about distance-speed-time relations in a kinematics unit and then about the ecological process relations between fish, duckweed, and bacteria occurring in a fish tank system. Results show learning gains in both science units, but this required a set of scaffolds to help students learn in this environment.

This paper combines Multi-Agent based simulation with causal modeling and reasoning to help students learn about ecological processes. Eighth grade students who took part in the study showed highly significant pre to post test gains on learning domain content and causal reasoning ability. Moreover, students’ success in reasoning with a causal model of the ecosystem was strongly correlated with higher learning gains. This work provides the foundations for designing scaffolded multi-agent, simulation-based intelligent learning environments with modeling and reasoning tools to help students learn science topics.