AI-based chatbots have the potential to accelerate learning and teaching, but may also have counterproductive consequences without thoughtful design and scaffolding. To better understand teachers’ perspectives on large language model (LLM) based chatbots, we conducted a study with 11 teams of middle-school teachers using chatbots for a science and computational thinking activity within a block-based programming environment. Based on a qualitative analysis of audio transcripts and chatbot interactions, we propose three profiles: explorer, frustrated, and mixed that reflect diverse scaffolding needs. In their discussions, we found that teachers perceived chatbot benefits such as building prompting skills and self confidence alongside risks including potential declines in learning and critical thinking. Key design recommendations include scaffolding the introduction to chatbots, facilitating teacher control of chatbot features, and suggesting when and how chatbots should be used. Our contribution informs the design of chatbots to support teachers and learners in middle school coding activities.

The flexibility and affordability of online, asynchronous, at-scale degree programs have significantly increased the accessibility of a master's-level graduate education. While studies have been conducted on the general growth of such programs and the quality of the online courses compared to their on-campus counterparts, few (if any) have examined outcomes such as alumni career growth or admission into other graduate programs. This work examines how one large online graduate program in computer science prepared alumni for matriculation into STEM PhD programs. Enrollment data from the National Student Clearinghouse was analyzed to identify key trends in alumni PhD enrollment. Surveys and interviews with program alumni were also conducted to investigate the unique paths that these individuals took to beginning their PhD education. This study finds that the program positively impacted alumni PhD experiences in STEM fields. Alumni noted that involvement with graduate research and coursework were key components in their preparation for a PhD program. These results demonstrate that an affordable, online, asynchronous graduate STEM program can provide non-traditional students with an effective pathway to PhD enrollment. The paper concludes with recommendations for asynchronous, at-scale degree programs seeking to expand their research opportunities for students with a desire to pursue PhD programs.

Research opportunities offer students at the master's level a chance to apply their knowledge, create projects for their portfolio, and to gain an understanding of the research process in preparation for potential doctoral studies. The traditional structure of these opportunities, however, is not trivial to translate to online programs with asynchronous delivery. As online programs grow, it is important to examine ways that the traditional benefits of these opportunities can be extended to a broader range of students. In this poster, we discuss our experience building the infrastructure and running early efforts at reaching this goal. We discuss several dedicated courses and seminars developed to offer larger scale opportunities for students to pursue research. We also discuss efforts in active development to provide better infrastructure support to reduce the friction and complexity of pursuing research at scale.

The research focuses in academia are typically determined from the top down, with professors focusing on projects that align with their existing lab or can be readily supported by grants. This approach is pragmatic, but this focus may not align with the bottom-up interests of the available student body. As part of a broader effort to expand the available research opportunities in a graduate program in computer science, this work focuses on collecting data on what is motivating student interest in research and what specific fields students desire to study with the intention of using the results for decision-making about how to allocate resources to best match student interests. This work reports on the responses of 143 graduate students in computer science at a major research institution in the United States in a course-based program where access to research opportunities is not guaranteed. It examines both the fields of particular interest and the self-reported motivations leading these students to attempt to seek out research opportunities.

Block-Based Programming (BBP) platforms, such as Snap!, have become increasingly prominent in $\mathrm{K}-12$ computer science education due to their ability to simplify programming concepts and foster computational thinking from an early age. While these platforms engage students through visual and gamified interfaces, teachers often face challenges in using them effectively and finding all the necessary features for classroom management. To address these challenges, we introduce SnapClass, a classroom management system integrated within the Snap! programming environment. SnapClass was iteratively developed drawing on established research about the pedagogical and logistical challenges teachers encounter in computing classrooms. Specifically, SnapClass allows educators to create and customize block-based coding assignments based on student skill levels, implement rubric-based auto-grading, and access student code history and recovery features. It also supports monitoring student engagement and idle time, and includes a help dashboard with a “raise hand” feature to assist students in real time. This paper describes the design and key features of SnapClass those are developed and those are under progress.

Background and Context. The importance of CS to 21st-century life and work has made it important to find ways to integrate learning CS and programming into the regular school day. However, learning CS is difficult, so teachers integrating programming need effective strategies to scaffold the learning. In this study, we analyze students’ log data and apply a novel technique to compare Parsons Problems with from-scratch programming in a middle school science class. Objectives. Our research questions aimed to investigate whether, how, and when Parsons Problems improve learning efficiency for a programming exercise within science, utilizing log data analysis and an automated progress detector (SPD). Method. We conducted a study on 199 students in a 6th-grade science course, divided into two groups: one engaged with Parsons problems, and the other, a control group, worked on the same programming task without scaffolding. Then, we analyzed differences in performance and coding characteristics between the groups. We also adopted an innovative application of SPD to gain a better understanding of how and when Parsons problems helped students make more progress on the coding task, with an objective measure of final student grades. Findings. The experimental group, with scaffolding through Parsons Problems, achieved significantly higher grades, spent significantly less time programming, and toggled less between block category tabs. Interestingly, they ran their code more frequently compared to the control group. The SPD analysis revealed that the experimental group made significantly higher progress in all four quartiles of their coding time. Implications. Our findings suggest that Parsons problems can improve learning efficiency by enhancing novices’ learning experience without negatively impacting their performance or grades, which is especially important when programming is integrated into K12 courses.

In this Birds of a Feather, we will discuss the roles of computing education in preparing students to understand and address the disparate impacts of climate change in local and global contexts. We intend to have open discussions on the challenges and opportunities related to connecting computing education with climate change and the injustices that climate change exacerbates. We will focus discussions around three questions: (1) How can we center justice-based perspectives on understanding and addressing climate change in computing education? (2) What are the relationships between computing, climate change, and overall environmental impacts? and (3) How should we reimagine traditional notions of "development" and "progress" in computing in ways that challenge how current framings within computing misunderstand or misteach computing's environmental impacts? We invite all computing educators, researchers, administrators, practitioners and anyone else with any level of curiosity about climate change to join this discussion (because it affects all of us!). Expected outcomes for this Birds of a Feather include sharing resources and experiences to build a community for knowledge sharing and collaborations.

As computer science is increasingly taught in secondary schools, tools need to integrate block-based environments into learning platforms. This way, teachers can more effectively lead lessons, help students, and assess students' programs in their classrooms. We conducted a participatory design process with three K-12 computing teachers to understand their struggle and needs for block coding within their classrooms. The teachers identified 14 needs that were not already addressed by our tool, SnapClass. SnapClass, a new web-based learning platform for Snap!, integrates assignments with starter code, executable student submissions, rubric-based assessment, and a gradebook into one platform. The teachers designed prototypes for three features important to their classrooms: assignment differentiation, help-requests, and peer and self-assessment. This paper begins by introducing SnapClass and the motivation for its development. Then through thematic analysis of the session transcripts, we identify the common struggles teachers face while instructing programming and summarize how they would address those struggles through the design of SnapClass.

There is a growing number of Ph.D. graduates whose research focuses on computing education, and they are significantly fueling the growth of the computing education research community. As more computing education Ph.D. graduates weigh their options on the job market, they are increasingly entering postdoctoral positions. For example, seven Computing Innovation Fellows positions over the last two years have been awarded to computing education researchers. Research shows that postdoctoral researchers have positive, cascading effects on research labs. However, despite the critical role they play in the research ecosystem, and the growing importance of postdoctoral positions in career development, there are no support structures for postdoctoral researchers in the computing education research community. This Birds-of-a-Feather session is an organized opportunity for postdoctoral researchers and those interested in postdoctoral positions to connect with one another, share postdoc experiences, and generate best practices. Attendees will have opportunities to discuss research goals and activities, career trajectories and opportunities, future conference and publication plans, pathways for future collaborations, and advice for the postdoc experience.

The COVID-19 pandemic has demonstrated that learning remotely is a crucial skill for K-12 students. However, remote instruction and collaboration bring a new set of challenges for these students, especially in the context of pair programming. An important goal for the CS education community is to understand these younger learners' experiences during remote programming activities. This experience report describes a three-day learning experience in which 18 middle school students engaged in remote pair programming activities by modeling scientific processes in a block-based programming language. After three remote pair programming sessions, we conducted individual interviews to understand middle school students' experiences during remote pair programming activities as well as comparing these new experiences to their previous co-located pair programming experiences. The results from these interviews suggest that the majority of the students (72%) enjoyed the remote activities despite many (55%) experiencing some form of technical difficulty. The interviews revealed important opportunities and challenges that being remote brought to pair programming within themes of changes in communication and focus, pair programming dynamics, and available resources. Students also identified issues with remote collaboration such as technical difficulties from software that impaired their ability to work and to communicate. These observations inform new efforts to adapt CS education to the increased demand for remote collaborative work and reveal patterns that may increase success in this new work style.

Computational modeling provides an excellent vehicle for raising scientific awareness of emergent and topical phenomena such as COVID-19. Now more than ever, it is crucial to provide students with factual information about how diseases spread and how their own actions can impact that spread. In order to both encourage computational thinking skills and build scientific knowledge of the COVID-19 pandemic, we have created a series of programming activities through which students construct their own computational models based on the emerging scientific consensus around COVID-19. Students are able to model everyday situations such as being in a crowded area or going to stores while unknowingly infected, and immediately see the consequences of those actions. By including accurate scientific variables such as the reproductive number of the virus, incubation period, and period of communicability, students are able to create their own epi-curves that demonstrate the severity of the disease and provide students with visual representation of how quickly COVID-19 spreads. We also use the scientific model and associated modeling activities to reinforce best practices at home and in the community. Finally, this curriculum development effort demonstrates how block-based computational modeling activities lend themselves to agile curricular re-design around emerging and topics of local interest

Despite the increasing attention to infusing CT into middle and high school content area classrooms, there is a lack of information about the most effective practices and models to support teachers in their efforts to integrate disciplinary content and CT principles. To address this need, this paper proposes the Code, Connect and Create (3C) professional development (PD) model, which was designed to support middle and high school content area teachers in infusing computational thinking into their classrooms. To evaluate the model, we analyzed quantitative and qualitative data collected from Infusing Computing PD workshops designed for in-service science, math, English language arts, and social studies teachers located in two Southeastern states. Drawing on findings from our analysis of teacher-created learning segments, surveys, and interviews, we argue that the 3C professional development model supported shifts in teacher understandings of the role of computational thinking in content area classrooms, as well as their self-efficacy and beliefs regarding CT integration into disciplinary content. We conclude by offering implications for the use of this model to increase teacher and student access to computational thinking practices in middle and high school classrooms.

Recent studies have demonstrated the prevalence of mental health issues and illnesses among students in higher education, especially in STEM degree programs like computing. While we work as a community towards solutions that benefit the mental health of all students, we must also take targeted action towards supporting students with diagnosed mental illnesses (e.g. major depression, bipolar disorder, schizophrenia). This should begin by allowing these often unheard students an opportunity to voice their invisible experiences in a safe space. This will start creating a shared understanding among faculty and students of how these illnesses can affect the educational experience, as well as how our common university practices can affect the lives of those living with these illnesses. This space will give students an understanding that their voices and experiences are valued and heard by the general academic community. We hope this will also show students that they are not alone and grant an opportunity for them to connect with others and establish networks of support. We will end this session discussing and disseminating best practices for supporting this community.

Pair Programming is often employed in educational settings as a means of promoting collaboration and scaffolding the assignment difficulty for teams. While much research supports its inclusion as a pedagogical practice at the university level, some research has demonstrated in K-12 contexts, it can potentially lead to inequitable learning enviroments and create dynamics between partners that might negatively effect novice learners. New block-based programming environments like Netsblox have attempted to address this by creating ways for both partners to program simultaneously, but this feature has yet to be examined in detail. In this paper, we introduce several modes of Collaboration afforded by Netsblox. This includes Pair-Separate, Pair-Together, and Partner Puzzles - a mode that Splits the necessary blocks to build the assignment between team members. From an initial pilot study involving 25 pairs of middle and high school students, we find that most pairs preferred working on assignments in the Partner Puzzle mode as it presented a fun challenge to teams. We end on recommendations for building assignments using this methodology and future research directions investigating the role of collaboration in programming

The Use-Modify-Create progression (UMC) was conceptualized in 2011 after comparing the productive integration of computational thinking across National Science Foundation-funded Innovative Technology Experiences for Students and Teachers (NSF ITEST) programs. Since that time, UMC has been widely promoted as a means to scaffold student learning of computational thinking (CT) while enabling personalization and allowing for creative adaptations of pre-existing computational artifacts. In addition to UMC's continued application, it has recently been utilized to scaffold student learning in topics as diverse as machine learning, e-textiles, and computer programming. UMC has also been applied to instructional goals other than "supporting students in becoming creators of computational artifacts." This panel will re-examine the UMC progression and refine our understanding of when its use is suitable, and when not, and share findings on evaluations and extensions to UMC that are productive in new and different contexts.

CSEd graduate students face a variety of unique issues. First, within a CS or information science program, this research area is not considered to be highly popular with respect to the quantity of CSEd faculty, graduate courses, and professionals immersed in this research area. Already, this presents a disadvantage compared to other peers in popular areas who have resources and/or a research community at their institutions to support them. Further, graduate students in this field are frequently one of the few, if not the only, CSEd researchers at their institutions. As a result, many graduate students have limited avenues to get consistent, relevant, critical feedback on their work, especially research-in-progress, i.e. research that has not already been published. In this Birds of a Feather (BoF) session, we aim to create and foster a safe and open environment that allows participants to get feedback on research-in-progress and share experiences and issues with being a graduate student in CSEd research. We invite genuine and honest conversations about the challenges facing rising academics and professionals in this area. Though the focus will be on CSEd research graduate students, any attendee who would like to hear about our experiences is welcome. We have two goals: (1) to cultivate mentorship relationships among participants to send them off with potential collaborators, supporters and advocates, and (2) to brainstorm ways to sustain these discussions in the ACM CSEd community.

This paper introduces Crescendo, a self-paced programming practice environment that combines the block-based and visual, interactive programming of Snap!, with the structured practices commonly found in Drill-and-Practice Environments. Crescendo supports students with Parsons problems to reduce problem complexity, Use-Modify-Create task progressions to gradually introduce new programming concepts, and automated feedback and assessment to support learning. In this work, we report on our experience deploying Crescendo in a programming camp for middle school students, as well as in an introductory university course for non-majors. Our initial results from field observations and log data suggest that the support features in Crescendo kept students engaged and allowed them to progress through programming concepts quickly. However, some students still struggled even with these highly-structured problems, requiring additional assistance, suggesting that even strong scaffolding may be insufficient to allow students to progress independently through the tasks.

This paper illustrates coding decisions by in-service teachers and high school interns working independently versus collaboratively to build computing activities for non-computing classrooms. We investigate code written in Snap! to gain insights on project type and subject matter. We also share case studies on how intern collaboration influences final product execution. Through our research, we found student-only teams often created tutorial projects whereas teachers-only teams create interactive narratives. We found students were able to reuse code across projects to replicate similar mechanics and that students specialize in different aspects of project creation. Overall, we find it beneficial to have collaborative teacher-student teams.

Research into intelligent programming systems has lead to numerous means of providing help to students during programming tasks but not in generating the right problem for students to work through. My work will be in developing and analyzing a programming problem generator for mastery learning that will leverage student data and incorporate methods for instructional design for programming tasks to give students the best problem to practice and achieve proficiency

In summer 2018, we conducted two week-long professional development workshops for 116 middle and high school teachers interested in infusing computational thinking (CT) into their classrooms. Teachers learned to program in Snap!, connect CT to their disciplines, and create infused CT learning segments for their classes. This paper investigates the extent to which teachers were able to successfully infuse CT skills of pattern recognition, abstraction, decomposition, and algorithms into their learning products.

Computational Thinking (CT) is being infused into curricula in a variety of core K-12 STEM courses. As these topics are being introduced to students without prior programming experience and are potentially taught by instructors unfamiliar with programming and CT, appropriate lesson design might help support both students and teachers. "Use-Modify-Create" (UMC), a CT lesson progression, has students ease into CT topics by first "Using" a given artifact, "Modifying" an existing one, and then eventually "Creating" new ones. While studies have presented lessons adopting and adapting this progression and advocating for its use, few have focused on evaluating UMC's pedagogical effectiveness and claims. We present a comparison study between two CT lesson progressions for middle school science classes. Students participated in a 4-day activity focused on developing an agent-based simulation in a block-based programming environment. While some classrooms had students develop code on days 2-4, others used a scaffolded lesson plan modeled after the UMC framework. Through analyzing student's exit tickets, classroom observations, and teacher interviews, we illustrate differences in perception of assignment difficulty from both the students and teachers, as well as student perception of artifact "ownership" between conditions.

Automated hints, a powerful feature of many programming environments, have been shown to improve students' performance and learning. New methods for generating these hints use historical data, allowing them to scale easily to new classrooms and contexts. These scalable methods often generate next-step, code hints that suggest a single edit for the student to make to their code. However, while these code hints tell the student what to do, they do not explain why, which can make these hints hard to interpret and decrease students' trust in their helpfulness. In this work, we augmented code hints by adding adaptive, textual explanations in a block-based, novice programming environment. We evaluated their impact in two controlled studies with novice learners to investigate how our results generalize to different populations. We measured the impact of textual explanations on novices' programming performance. We also used quantitative analysis of log data, self-explanation prompts, and frequent feedback surveys to evaluate novices' understanding and perception of the hints throughout the learning process. Our results showed that novices perceived hints with explanations as significantly more relevant and interpretable than those without explanations, and were also better able to connect these hints to their code and the assignment. However, we found little difference in novices' performance. Our results suggest that explanations have the potential to make code hints more useful, but it is unclear whether this translates into better overall performance and learning.

One way to increase access to education on computing is to integrate computational thinking (CT) into K12 disciplinary courses. However, this challenges teachers to both learn CT and decide how to best integrate CT into their classes. In this position paper, we present PRADA, an acronym for Pattern Recognition, Abstraction, Decomposition, and Algorithms, as a practical and understandable way of introducing the core ideas of CT to non-computing teachers. We piloted the PRADA model in two, separate, week-long professional development workshops designed for in-service middle and high school teachers and found that the PRADA model supported teachers in making connections between CT and their current course material. Initial findings, which emerged from the analysis of teacher-created learning materials, survey responses, and focus group interviews, indicate that the PRADA model supported core content teachers in successfully infusing CT into their existing curricula and increased their self-efficacy in CT integration.

Program Visualizations (PVs) have been used as educational tools to allow students to visually inspect the runtime behavior of their code. However, many of these systems act as low-level visual debuggers not high-level abstractions of program behavior. Additionally, evaluations of these systems tend to focus more on student engagement or opinion in using the system and not on artifacts produced using the system. This paper discusses the effectiveness of a PV developed to aide students in an undergraduate Artificial Intelligence class on a pathfinding homework assignment. Students in 4 semesters of the course were tasked to develop pathfinding algorithms for an agent to navigate worlds in cases of both certain and uncertain world information. Students in 2 semesters of the course were given access to a PV that allowed them to see a visual representation of their agent navigating the world in either information condition. The final agents developed by these students were compared with those developed by students who never received the PV. Comparisons were made on the performance of these agents in both cases of uncertain and certain world information on several test worlds. Student written reports for the Experimental condition were also analyzed. The results showed significant differences in the performance of the algorithms developed in both certain and uncertain world information. Student reflections on using the PV within the written reports provide insight into how the PV informed the design and development of their submission.

To provide K-12 computing education for all, programming cannot be regulated to only computer science classes. Instead, computing education must be infused into all other required courses reaching all students through curriculum-integrated computing activities. This will pose challenges as students in these settings have an increasingly wide range of programming backgrounds. Additionally, teachers must feel comfortable not only teaching programming, but doing so in a meaningful context that integrates coding into their course topics. To truly meet the scale of this challenge, teachers will need to be able to develop meaningful integrated programming lessons, and have the supports necessary to implement them in a wide-variety of classes with students of vastly different skill-levels. This research will investigate two major means of aiding the rapid development and deployment of integrated block-based programming lessons in K-12 courses. The first will be through exploring design patterns for these integrated lessons, specifically focusing on how different activity types, scaffolding, and progressions affect classroom and student programming outcomes. The second will be in the research and development of generalized data-driven algorithms that will support novice teachers and students during programming in a wide-variety of block-based activities.

With increased numbers of K-12 computing courses, we also see an increase in teachers new to the subject, making it difficult for them to properly assess student programming assignments. Many of these teachers require project-specific rubrics to help assess student learning. Researchers have attempted to create systematic, validated, and reliable rubrics for these courses with only minor success. In this research, we make an argument for the validity of our low-stakes computing rubrics. In doing so, we establish a validated method for creating a full-suite of project-based rubrics for K-12 computing courses, helping teachers, researchers, and practitioners make much-needed course materials. Evaluating these rubrics, we see grader consistency as well as heatmaps of where teachers are looking for computational thinking concepts in code.

Instructional supports (Supports) help students learn more effectively in intelligent tutoring systems and gamified educational environments. However, the implementation and success of Supports vary by environment. We explored Support design in an educational programming game, BOTS, implementing three different strategies: instructional text (Text), worked examples (Examples) and buggy code (Bugs). These strategies are adapted from promising Supports in other domains and motivated by established educational theory. We evaluated our Supports through a pilot study with middle school students. Our results suggest Bugs may be a promising strategy, as demonstrated by the lower completion time and solution code length in assessment puzzles. We end reflecting on our design decisions providing recommendations for future iterations. Our motivations, design process, and study's results provide insight into the design of Supports for programming games.