There is growing recognition of the need to teach artificial intelli- gence (AI) and machine learning (ML) at the school level. This push acknowledges the meteoric growth in the range and diversity of ap- plications of ML in all industries and everyday consumer products, with Large Language Models (LLMs) being only the latest and most compelling example yet. Efforts to bring AI, especially ML educa- tion to school learners are being propelled by substantial industry interest, research efforts, as well as technological developments that make sophisticated ML tools readily available to learners of all ages. These early efforts span a variety of learning goals captured by the AI4K12 "big ideas" framework and employ a plurality of pedagogies.This paper provides a sense for the current state of the field, shares lessons learned from early K-12 AI education as well as CS education efforts that can be leveraged, highlights issues that must be addressed in designing for teaching AI in K-12, and provides guidance for future K-12 AI education efforts and tackle what to many feels like "the next new thing".

Even as efforts to promote K-12 CS education forge ahead, there is a growing consensus that students must also be taught artificial intelligence (AI) and machine learning (ML) in order to be prepared for the fast-changing world powered by AI/ML. How can ensure that we leverage learnings from two decades of CS education research and practice, and build on successes while mitigating missteps? This panel invites researchers with deep expertise in 'CSForAll' efforts for a timely discussion and sharing of valuable lessons from CS education efforts about pedagogies, attention to equity, and teacher preparation that will also benefit K-12 AI education.

Existing approaches to teaching artificial intelligence and machine learning (ML) often focus on the use of pre-trained models or fine-tuning an existing black-box architecture. We believe ML techniques and core ML topics, such as optimization and adversarial examples, can be designed for high school age students given appropriate support. Our curricular approach focuses on teaching ML ideas by enabling students to develop deep intuition about these complex concepts by first making them accessible to novices through interactive tools, pre-programmed games, and carefully designed programming activities. Then, students are able to engage with the concepts via meaningful, hands-on experiences that span the entire ML process from data collection to model optimization and inspection. This paper describes our 'AI & Cybersecurity for Teens' suite of curricular activities aimed at high school students and teachers.

Motivation. We have created a modular project-based learning curriculum, Computer Science Frontiers (CSF) [1, 8], for secondary students in attempts to increase the persistence of computer science (CS) students in higher education. The CSF course is divided into four different modules (Distributed Computing, Internet of Things, Artificial Intelligence, and Software Engineering), each centered around a topic typically introduced to students only in higher education. Using the block-based programming environment NetsBlox [4], students are able to access various Application Programming Interfaces related to their interests [2, 3]. The goal of this course is to increase student interest in CS during high school - when first career choices occur [7] - in hopes they will persist in CS during their undergraduate studies.

Artificial Intelligence (AI) and cybersecurity are becoming increasingly intertwined, with AI and Machine Learning (AI/ML) being leveraged for cybersecurity, and cybersecurity helping address issues caused by AI. The goal in our exploratory curricular initiative is to dovetail the need to teach these two critical, emerging topics in highschool, and create a suite of novel activities, 'AI & Cybersecurity for Teens' (ACT) that introduces AI/ML in the context of cybersecurity and prepares high school teachers to integrate them in their cybersecurity curricula. Additionally, ACT activities are designed such that teachers (and students) build a deeper understanding of how ML works and how the machine actually "learns". Such understanding will aid more meaningful interrogation of critical issues such as AI ethics and bias. ACT introduces core ML topics contextualized in cybersecurity topics through a range of programming activities and pre-programmed games in NetsBlox, an easy-to-use block-based programming environment. We conducted 2 pilot workshops with 12 high school cybersecurity teachers focused on ACT activities. Teachers' feedback was positive and encouraging but also highlighted potential challenges in implementing ACT in the classroom. This paper reports on our approach and activities design, and teachers' experiences and feedback on integrating AI into high school cybersecurity curricula.

Formative classroom assessments are those formal and informal moves teachers make in order to make inferences about what their students know and can do, and are meant to benefit the learning process through feedback and adjustments to classroom teaching. The ability to recognize and address errors in students' thinking through the use of formative assessment makes formative classroom assessment literacy extremely important. Studies have identified huge gaps in formative assessment literacy for K-12 CS teachers. This paper describes the design of a 4-session K-12 CS teacher PD module 'Formative Classroom Assessment for Teachers' (FCAT) focusing on developing teachers' formative assessment literacy. The sessions cover the need for formative assessment, elements of good formative assessment, and how to design them based on K-12 CS learning goals and known student difficulties and misconceptions along with examples.We piloted FCAT and also conducted sessions on specific modules at CSTA reaching ~500 participants. In addition to rationale and design of FCAT, this poster will share teacher feedback on the FCAT module which was overwhelmingly positive and also helped us identify future improvements to FCAT.

Historically, female students have shown low interest in the field of computer science. Previous computer science curricula have failed to address the lack of female-centered computer science activities, such as socially relevant and real-life applications. Our new summer camp curriculum introduces the topics of artificial intelligence (AI), machine learning (ML) and other real-world subjects to engage high school girls in computing by connecting lessons to relevant and cutting edge technologies. Topics range from social media bots, sentiment of natural language in different media, and the role of AI in criminal justice, and focus on programming activities in the NetsBlox and Python programming languages. Summer camp teachers were prepared in a week-long pedagogy and peer-teaching centered professional development program where they concurrently learned and practiced teaching the curriculum to one another. Then, pairs of teachers led students in learning through hands-on AI and ML activities in a half-day, two-week summer camp. In this paper, we discuss the curriculum development and implementation, as well as survey feedback from both teachers and students.

Existing approaches to teaching artificial intelligence and machine learning often focus on the use of pre-trained models or fine-tuning an existing black-box architecture. We believe advanced ML topics, such as optimization and adversarial examples, can be learned by early high school age students given appropriate support. Our approach focuses on enabling students to develop deep intuition about these complex concepts by first making them accessible to novices through interactive tools, pre-programmed games, and carefully designed programming activities. Then, students are able to engage with the concepts via meaningful, hands-on experiences that span the entire ML process from data collection to model optimization and inspection.

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.

The Computer Science Frontiers (CSF) project introduces teachers to the topics of artificial intelligence and distributed computing to engage their female students in computing by connecting lessons to relevant cutting edge technologies. Application topics include social media and news articles, as well as climate change, the arts (movies, music, and museum collections), and public health/medicine. CSF educators are prepared in a pedagogy and peer-teaching centered professional development program where they simultaneously learn and teach distributed computing, artificial intelligence, and internet of things lessons to each other. These professional developments allow educators to hone in on their teaching skills of these new topics and gain confidence in their ability to teach new computer science materials before running several activities with their students in the academic year classroom. In this workshop, teachers participating in the CS Frontiers professional development will give testimonials discussing their experiences teaching these topics in a two week summer camp. Attendees will then try out three computing activities, one from each Computer Science Frontiers module. Finally, there will be a question and answer session.

There is a growing recognition that quiz-like formative assessments can play a crucial role in providing timely feedback on student learning in K-12 CS classrooms. While programming projects are necessary, scoring them and providing pointed feedback to students to improve learning requires enormous effort. Besides, code-reading and comprehension exercises are just as important as code creation when learning coding. Auto-gradable exit tickets in K-12 CS classrooms have thus far stayed in the realm of multiple-choice questions (MCQ). Our NSF-funded project set out to explore how we can push the boundaries on formative assessments that are engaging and interactive for students and can be used (like MCQ) to get quick feedback on student understanding We drew on CS education research to create a suite of new assessments types designed to support student interactivity on the assessment platform edfinity.com. This poster shares examples of these new problem types including point-and-click/hotspot interaction, match the column, Parson's problems, and problems that allow students to match subgoal labels to sections of code. Given that these are targeted for K-12 classrooms, many problem types such as point-and-click and subgoal labeling are well-suited for images of block-based code snippets. Additionally, with younger learners and neuro/ethnic diversity in mind, these assessments also support rich text, images and videos in problems (including Parson's and match-the-column option blocks) as well as solution explanations of the problem. Research on the use of these assessments is currently underway; early feedback from teachers attending our workshops has been very positive

Providing learners with authentic interdisciplinary experiences is one strategy to foster positive perceptions of CS as a discipline that supports a breadth of applications. We designed a high school mini course using climate science as an interdisciplinary context, since it is of interest to today's youth and provides opportunities to engage with authentic applications of computing that leverage real data to examine issues. The course was designed to engage students in climate change, data visualization, distributed computing, and motivate the use of advanced data abstractions and practices. We used NetsBlox, an extension of Snap! that makes real datasets & Web services accessible through easy-to-use blocks. A paleoclimatologist led climate science discussions and research questions with students. Post-survey responses to questions probing insights students gained shed light on the positive impact on students' perceptions of CS through this interdisciplinary experience, and how it expanded their horizons for future STEM inquiry using data and computing. We share curricular details for use in high schools and takeaways to help promote rich programming experiences that improve students' perceptions of computing.

In this special session, we present tools and resources created from educational theory to make teaching and scholarship easier and more effective. Included in these resources are CS Teachers Association, CSforAll, CS Teaching Tips, CT Pathways Toolkit, EngageCSEdu, CS Assessments Hub, Cybersecurity, and CS Ed Research Resource Center. These tools/resources provide assistance at the district and school level (administrators, curriculum designers), classroom level (teachers), and researcher/evaluator level. In addition to these overviews, we will provide information on where to find them and will leave ample time for questions from attendees.

Unlike summative assessment that is aimed at grading students at the end of a unit or academic term, formative assessment is assessment for learning, aimed at monitoring ongoing student learning to provide feedback to both student and teacher, so that learning gaps can be addressed during the learning process. Education research points to formative assessment as a crucial vehicle for improving student learning. Formative assessment in K-12 CS and programming classrooms remains a crucial unaddressed need. Given that assessment for learning is closely tied to teacher pedagogical con- tent knowledge, formative assessment literacy needs to also be a topic of CS teacher PD. This position paper addresses the broad need to understand formative assessment and build a framework to understand the what, why, and how of formative assessment of introductory programming in K-12 CS. It shares specific pro- gramming examples to articulate the cycle of formative assessment, diagnostic evaluation, feedback, and action. The design of formative assessment items is informed by CS research on assessment design, albeit related largely to summative assessment and in CS1 contexts, and learning of programming, especially student misconceptions. It describes what teacher formative assessment literacy PD should entail and how to catalyze assessment-focused collaboration among K-12 CS teachers through assessment platforms and repositories.

The AP Computer Science Principles (CSP) high school course introduces students to computer science and programming. What should motivated students study after successful completion of AP CSP? The AP CSA class teaches Java programming and it has traditionally not attracted students from underrepresented groups. We are working on an alternative, projects-based course that will teach cutting edge CS concepts, such as distributed computing, computer networking, cybersecurity, the internet of things and machine learning, in a hands-on, accessible manner. Such an approach enables students to work on problems that interest them making computing more relevant and the curriculum more engaging. We utilize NetsBlox, a collaborative, block-based programming environment that extends Snap! with a few carefully selected abstractions that open up the vast array of resources freely available on the internet for student programs. Moreover, the tool enables students to work together on the same project remotely similarly to how Google Docs operate. This demonstration will introduce the environment and highlight its utility in creating distributed applications such as a shared whiteboard app and projects that access public domain scientific data sources and visualize them in various ways using online services such as Google Maps or charting. More information is available at https://netsblox.org.

Many block-based programming environments have proven to be effective at engaging novices in learning programming. However, most restrict access to the outside world, limiting learners to commands and computing resources built in to the environment. Some allow learners to drag and drop files, connect to sensors and robots locally or issue HTTP requests. But in a world where most of the applications in our daily lives are distributed (i.e., their functionality depends on communicating with other programs or accessing resources and data on the internet), the lack of support for beginners to envision and create such distributed programs is a lost opportunity. This paper argues that it is not only feasible, but crucial, to create environments with simple yet powerful abstractions that open up distributed computing and other widely used but advanced computing concepts including networking, the Internet of Things, and cybersecurity to novices. By thus removing the walls around our environments, we can expand opportunities for learning considerably: programs can access a wealth of online data and web services, and communicate with other projects. Moreover, these changes can enable young learners to collaborate with each other during program construction whether they share their physical location or study remotely. Importantly, providing access to the wider world will also help counter widespread student perceptions that block-based environments are mere toys, and show that they are capable of creating compelling applications. The paper presents NetsBlox, a programming environment that supports these ideas and shows that tools can be designed to democratize access to powerful ideas in computing.

Teaching of computer science (CS is rapidly expanding in schools. Learning to program is a key ingredient of school CS curricula, and consequently, there is a need for quality measures of student learning of foundational programming concepts. However, high-quality tools for measuring student learning in introductory CS have been under-developed and under-researched. This experience report shares the process of design and refinement of a summative paper-based assessment (that could also be administered online) for introductory programming in middle grades (6-8). We share our experiences with the use of assessment as a pre-post measure in a middle school introductory programming course in diverse, urban school classrooms in the US and use that data to conduct validity, reliability and item discrimination analyses.

Policymakers believe that preparing all students from the earliest grades to high school for a new future of STEM+Computing (STEM+C) integration involves teaching them not only the science and math central to these areas, but also how computational thinking is integral to STEM disciplines. This panel brings together four researchers who focus on research and development of interdisciplinary approaches to the integration of computing within STEM teaching and learning for preK-12 students. They will share the most impactful, practical, and promising approaches to STEM+C integration, their pros and cons, challenges, and key insights to successful STEM+C integration at all grade levels.

The analysis of data plays an increasingly critical role in computing education research, enabled by more and larger datasets, more powerful analysis techniques and better infrastructure for sharing. This panel brings together four panellists at various stages of work involving the collection and analysis of large datasets in different fields of computing education. The panellists will each discuss the current state of their work, the unique aspects of their data, and how that data fits into the larger landscape of computing education and research. Panellists will be asked to explain how they are employing AI and data mining techniques to learn about learners, the research methods they have used to make this happen, and any significant key findings they have discovered through this processes. The panel will discuss emerging topics, including: going beyond log data, handling global-scale datasets, efficiently collaborating with cross-dataset analysis, and ethical and privacy considerations. After the panelists present (5 minutes each), the moderator will pose follow-up questions and invite the audience to pose additional questions or provide other feedback. Key takeaways will include how data mining and artificial intelligence can contribute to improved insight and learning gains and how the larger computer education community can participate in data collection or analysis.

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.

Computational thinking (CT) is a means to help learners engage in authentic disciplinary and problem-solving practices of computer science (CS). For CS classrooms, CT is considered "thinking like a computer scientist". CT is believed to be an important learning goal of introductory CS in addition to CS concepts and programming. Despite the growing attention on CT in K-12 CS education, there is lingering confusion on the what and how of CT, and CT's relationship to coding and CS. Education research on disciplinary thinking skills in science and mathematics education can provide guidance for teaching and learning of CT. For example, (a) The shift in emphasis on disciplinary thinking helps focus on deeper conceptual understanding rather than rote learning of knowledge and facts. Hence thinking like a scientist, or mathematician, historian or computer scientist, draws attention to authentic practices of those disciplines. (b) Thinking skills are best taught in context. Therefore, CT should be taught in CS classrooms or integrated into learning of other subjects rather than taught as a separate skill or subject. (c) Even if there is no transfer beyond the context in which they are taught, a focus on thinking skills helps in deeper conceptual learning; (d) Like critical or creative thinking, CT should be integrated into other subjects to enrich learning. Research on meaningful technology integration across subjects provides useful frameworks to inform CT integration efforts. This talk aims to productively move the discourse on CT toward concrete ideas for K-12 educators, researchers, and curricular designers.

How do we integrate requisite thinking skills into children's earliest informal and formal learning experiences in order to prepare the next generation of problem solvers? This project examines how Computational Thinking (CT) skills align to the abilities and interests of young children and can be integrated with other STEM learning activities in order to promote school readiness. The goals of the project are to (1) build a knowledge base on how CT can be promoted in early childhood through integration with mathematics and science, (2) develop learning blueprints to guide the development of integrated classroom and family resources, and (3) develop and evaluate prototype activities to identify design principles useful to future development efforts integrating CT and STEM in early childhood. In this work, a multidisciplinary research team with expertise in CT, early science and mathematics, technology and media, and home and school learning partners with preschool educators and parents to co-design and pilot test prototype activities. Emerging prototypes presented integrate fundamental science and mathematics concepts and practices with CT skills such as problem decomposition, logical and algorithmic thinking, abstraction and debugging. They include hands-on activities with manipulatives familiar to young children and developmentally appropriate technology with unique affordances for complementing and reinforcing the learning that occurs during hands-on experiences. With a goal of addressing CS For All, the co-design, development and pilot testing of prototype activities aim to take into account the needs and strengths of preschool programs and families from low-income and culturally diverse communities.

Programming-in a text-based or visual language-is a complex activity that novices find difficult to learn. In this paper, we present an experience report on how novel activities designed for early conceptual exploration can support middle school students' engagement with and learning of foundational programming concepts beginning programmers often struggle with, specifically, variables, expressions (Boolean, arithmetic, relational), loops, and abstraction. We drew on mathematics education research on dynamic representations to design and develop a set of four non-programming, (open access) web-based, interactive activities and micro-worlds and two unplugged activities as part of a suite of activities that can be embedded in a typical introductory programming curriculum to introduce learners to the target concepts before they encounter them in programming. This report describes the activities as well as our experience with their use as part of an introductory computer science course in three diverse, urban, middle school classrooms in the US

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."

No abstract available.

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.

editorial Free Access Share on Unlocking the Potential of Learning Analytics in Computing Education Authors: Shuchi Grover SRI International SRI InternationalView Profile , Ari Korhonen Aalto University Aalto UniversityView Profile Authors Info & Claims ACM Transactions on Computing EducationVolume 17Issue 3September 2017 Article No.: 11epp 1–4https://doi.org/10.1145/3122773Published:28 August 2017Publication History 4citation670DownloadsMetricsTotal Citations4Total Downloads670Last 12 Months64Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The term “Cyberlearning” is used in the United States to describe a community of researchers, largely funded by the US National Science Foundation, who are exploring the integration of computer science research with learning sciences research. The Cyberlearning community is parallel to the EC-TEL community and the purpose of this poster is to foster mutual engagement between the communities. The paper describes the origin of the term, the conception of the field, the kinds of research being conducted, and some of the exemplary projects. The paper will also introduce the Center for Innovative Research in Cyberlearning (CIRCL), which is the hub of the knowledge network (research community) for cyberlearning and hosts a useful collection of resources.

Collaborative problem solving (CPS) is seen as a key skill in K-12 education---in computer science as well as other subjects. Efforts to introduce children to computing rely on pair programming as a way of having young learners engage in CPS. Characteristics of quality collaboration are joint exploring or understanding, joint representation, and joint execution. We present a data driven approach to assessing and elucidating collaboration through modeling of multimodal student behavior and performance data.

Authentic problem solving tasks in digital environments are often open-ended with ill-defined pathways to a goal state. Scaffolds and formative feedback during this process help learners develop the requisite skills and understanding, but require assessing the problem-solving process. This paper describes a hybrid approach to assessing process at scale in the context of the use of computational thinking practices during programming. Our approach combines hypothesis-driven analysis, using an evidence-centered design framework, with discovery-driven data analytics. We report on work-in-progress involving novices and expert programmers working on Blockly games.

In this paper, we describe research conducted around a 7-week curriculum designed to introduce middle school students to computer science with a focus on algorithmic thinking and programming. The pedagogical ideas employed in this curriculum were drawn from past research. Empirical investigations over two studies in a public middle school in the US examined changes in students' understanding of algorithmic constructs and the factors affecting that learning. Multi-level analyses revealed that students in both studies (1) achieved substantial learning gains in algorithmic thinking skills and significant growth towards a more mature understanding of computing as a discipline, and (2) found certain CT ideas and constructs more difficult than others. Prior computing experiences and math and English ability were found to be predictors of learning outcomes. Extracurricular experiences with technology also appeared to impact outcomes.

Nationwide, efforts are focusing on taking computer science (CS) to scale in high school classrooms through the Exploring Computer Science (ECS) and AP CS Principles (CSP) courses. Recent inroads are also being made to take structured introductory curricula to middle school classrooms. Often, a starting point for teaching CS in middle and high school is a discussion around the seemingly simple question "What is a computer?" The question is aimed to help learners understand through debate and discussion what makes a computer a computer. This paper reports our analysis of (a) middle school students' discussions around this question, and (b) high school students' responses to an assessment question measuring this understanding. Our analyses of students' comments and responses reveal that a discussion around "what is a computer?" may be problematic for students, as it tends to focus on the tool, the "computer." We suggest that the discussion needs re-framing to focus instead on computing and computation.

Computational Thinking (CT), now widely recognized as a necessary skill for today's generation of learners, is typically being introduced to students in middle and high school (and even at the college level) in the context of programming in visual, block-based, drag-drop environments such as Scratch, Alice, Blockly, Tynker, Agentsheets and Kodu. How well are these first experiences preparing students to apply their learning successfully to future computational experiences, which are likely to be in the context of higher-order, text-based programming languages? How can we teach so as to facilitate successful 'transfer of learning' to future contexts? The art of teaching for transfer, or mediating transfer, has been actively studied for decades in education research. Pedagogies such as 'bridging', 'hugging', and 'preparation for future learning' (PFL) have shown promise in the context of education research in STEM domains at the secondary level. This fun lightning talk will shed light on these very pertinent ideas that CS education will benefit from, and share concrete strategies that can be employed in K-12 and college-level introductory programming classrooms to prepare students for better success with future computational experiences. The broader goal is to get CS educators and researchers thinking about an important aspect of teaching introductory programming and get a conversation started.

As computer science (CS) and computational thinking (CT) make their way into all levels of K-12 education, valid assessments aligned with new curricula can assist in measuring student learning, easing the way for adoption of new computing courses, and evaluation of pedagogical approaches for teaching computing ideas and concepts. Without attention to rigorous assessment, CT can have little hope of making its way successfully into K-12 school education settings at scale. This BoF session will involve discussion around ongoing work at SRI International (under several NSF-funded projects) on the design and development of formative and summative assessments for the ECS curriculum. Additionally, various forms of assessment (including free response and multiple-choice questions, and computational artifacts), and insights from past research on their use will be discussed. BOF attendees will be able to discuss multiple viewpoints, connect with others who care about assessment of CT, and share resources and ideas.

CS teacher development has become a major effort for the SIGCSE community in part due to NSF's CS10K efforts and expanding CSTA involvement. However there are few examples of university courses explicitly designed to train CS teachers. We do not yet have clarity on the topics CS education methods courses should cover and how best to prepare teachers to teach learners new to computational problem solving and programming. As an interdisciplinary field of study, CS education must necessarily draw on domain knowledge in CS, research in computing education, as well as research in education and the learning sciences about how students learn, both generally and in computing. At the same time, a methods course must provide prospective teachers with practical, hands-on experiences wherein they integrate research-based best practices with age-appropriate content for their target student population.

As computing curricula continue to make their way into K-12 schools, the issue of assessing student learning of computational concepts remains a thorny one. This paper describes the multiple forms of assessments used in a 6-week middle school curriculum with the goal of capturing a holistic view of student learning. A key aspect of this research is the use of instruments developed and shared in prior research. Included among these were several questions used in an Israeli nationwide exam to test middle school student learning of programming in Scratch. This paper reports on the use of the curriculum in two studies conducted in a public US middle school classroom, and compares performances of these students with those reported by the Israeli Ministry of Education in their large-scale study. It also argues for multiple modes of assessment of computational learning in K-12 settings.

Lack of teachers to teach computer science (CS) and pedagogically sound introductory CS curricula remain a significant challenge facing secondary schools attempting to teach CS. This paper describes our efforts to design and pilot an online 6-week middle/high school course using Stanford's OpenEdX platform. The pedagogy, curriculum and assessment are guided by learning theory. The course leverages OpenEdX features for contextual discussions and multiple-choice assessments that promote student learning and provide feedback. The paper reports on experiences in using instructor dashboards to identify targets of student difficulty and to aid curriculum redesign.

Past research extensively points to gross misperceptions of the discipline of Computer Science among students in middle and high school. As efforts to introduce computing education in K-12 gains traction in tandem with initiatives that address issues of interest and attitudes towards CS, misperceptions of computing as a discipline must also be addressed as early as middle school, which is known to be a key time for identity building. This paper shares the results of a curricular intervention that aims to show CS to students in a new light - in real world contexts and as a creative and problem-solving discipline; as something bigger and broader than the "computer-centric" view that students are known to harbor.

Past research on children and programming from the 1980s called for deepening the study of the pedagogy of programming in order to help children build better cognitive models of foundational concepts of CS. More recently, computing education researchers are beginning to recognize the need to apply the learning sciences to develop age- and grade-appropriate curricula and pedagogies for developing computational competencies among children. This paper presents the curriculum of an exploratory workshop that employed a discourse-intensive pedagogy to introduce middle school children to programming and foundational concepts of computer science through programming mobile apps in App Inventor for Android (AIA).